Article

A Method of Coding Television Signals Based on Edge Detection

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Abstract

A method is described for transmitting digitalized video signals to reduce channel capacity from that needed for standard PCM. This method takes advantage of the inability of the human eye to notice the exact amplitude and shape of short brightness transients. The transmitted information consists of the amplitudes and times of occurrence of the “edge” points of video signals. These selected samples are coarsely quantized if they belong to high-frequency regions, and the receiver then interpolates straight lines between the samples. The system was simulated on the IBM 704 computer. The processed pictures and obtained channel-capacity savings are presented.

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... The basic idea of most available edge detectors is to locate some local object-boundary information in an image by thresholding and skeletonizing the pixel-intensity variation map. Since the earliest work by Julez [1] in 1959, a huge number of edge detectors has been developed from different perspectives (e.g., [2] [3] [4] [5] [6] [7] [8] [9]). A very natural and important question is then: which edge detector and detector-parameter settings can produce better edgedetection results? ...
... The numerator, |A∩B|, measures how much the true object region is detected. The denominator, |A∪B|, is a normalization factor which normalizes the performance measure to the range of [0] [1]. A performance of 1 is achieved if and only if the detected boundary completely coincides with the ground-truth boundary, that is, A = B. Zero performance indicates that there is no region-intersection between the detected object and the ground-truth object. ...
... The gradient-magnitude confidence ρ is calculated by counting the percentage of pixels that have a gradient magnitude less than that of the considered edge. Both confidence measures take values in the range of [0] [1]. In general, the Edison edge detector uses an approach similar to Canny to locate edge pixels and the major difference lies in that the Edison edge detector incorporates these two confidence measures in the hysteresis step. ...
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Edge detection has been widely used in computer vision and image processing. However, the performance evaluation of the edge-detection results is still a challenging problem. A major dilemma in edge-detection evaluation is the difficulty to balance the objectivity and generality: a general-purpose edge-detection evaluation independent of specific applications is usually not well defined, while an evaluation on a specific application has weak generality. Aiming at addressing this dilemma, this paper presents new evaluation methodology and a framework in which edge detection is evaluated through boundary detection, that is, the likelihood of retrieving the full object boundaries from this edge-detection output. Such a likelihood, we believe, reflects the performance of edge detection in many applications since boundary detection is the direct and natural goal of edge detection. In this framework, we use the newly developed ratio-contour algorithm to group the detected edges into closed boundaries. We also collect a large data set (1030) of real images with unambiguous ground-truth boundaries for evaluation. Five edge detectors (Sobel, LoG, Canny, Rothwell, and Edison) are evaluated in this paper and we find that the current edge-detection performance still has scope for improvement by choosing appropriate detectors and detector parameters.
... (2)Edge-based Segmentation Edge-based segmentation achieves image segmentation by detecting edges of different regions. Edge detection was first introduced by Julez [12] in 1959. In 2010, Li [13] proposed optimization criteria based on the OTSU algorithm to segment small target defects. ...
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Early screening methods for the thyroid gland include palpation and imaging. Although palpation is relatively simple, its effectiveness in detecting early clinical signs of the thyroid gland may be limited, especially in children, due to the shorter thyroid growth time. Therefore, this constitutes a crucial foundational work. However, accurately determining the location and size of the thyroid gland in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is poor agreement on thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice. In order to extract sufficient texture information from pediatric thyroid ultrasound images while reducing the computational complexity and number of parameters, this paper designs a novel U-Net-based network called DC-Contrast U-Net, which aims to achieve better segmentation performance with lower complexity in medical image segmentation. The results show that compared with other U-Net-related segmentation models, the proposed DC-Contrast U-Net model achieves higher segmentation accuracy while improving the inference speed, making it a promising candidate for deployment in medical edge devices in clinical applications in the future.
... Edge-based segmentation is also a commonly used method for image segmentation, and its fundamental idea is to achieve segmentation by detecting the edges of different regions in the image. Edge detection technology first appeared in 1959 when Julez [9] and others introduced the concept of edge detection in their paper "A method for the encoding of television signals based on edge detection." Since then, edge detection technology has gradually gained widespread application and plays a crucial role in the field of image segmentation. ...
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The examination methods for the thyroid include laboratory tests and imaging studies. Although laboratory and imaging examinations are relatively straightforward, their effectiveness in detecting early clinical symptoms of the thyroid may be limited, especially in children due to the shorter growth time of the pediatric thyroid. Therefore, this constitutes a crucial foundational work. However, accurately determining the position and size of the thyroid in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is significant variation in thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice.
... Since B. Julez completed his work in 1959 [20], there has much literature on edge detection, and object edge detection has long been of interest. According to the different ways of extracting edge features, these methods can be categorized as edge differential operators, traditional machine learning methods, or deep learning methods. ...
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The edge detection model based on deep learning significantly improves performance, but its generally high model complexity requires a large pretrained Convolutional Neural Networks (CNNs) backbone, and hence large memory and computing power. To solve this problem, we carefully choose proper components for edge detection, introduce a Multiscale Aware Fusion Module based on self-attention and a feature-unmixing loss function, and propose a lightweight network model, Pixel Difference Unmixing Feature Networks (PDUF). The backbone network of proposed model is designed to adopt skip long-short residual connection and does not use pre-trained weights, and requires straightforward hyper-parameter settings. Extensive experiments on the BSDS, NYUD, and Multi-cue datasets, we found that the proposed model has higher F-scores than current state-of-the-art lightweight models (those with fewer than 1 million parameters) on BSDS500 (ODS F-score of 0.818), NYUDv2 depth datasets (ODS F-score of 0.767) and Multi-Cue dataset (ODS F-score 0.871(0.002)), with similar performance compared with some large models (with about 35 million parameters).
... Such a local discontinuity of the intensity distribution is recognized as an edge point. Bela Julesz [10] proposed that by connecting the nearby edge points by a line (preferably straight line), one may generate an edge. A number of edge-detecting filters have been proposed which include different techniques, methodologies and perspectives. ...
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The pattern of spatial contrast discontinuities in natural images has been analysed in the present work, and based on it, a new adaptive model of the bio-inspired Difference of Gaussian (DOG)-based edge detector has been designed. The distinguishing feature of the proposed filter is that the magnitude of surround suppression in receptive field of the DOG is adaptively adjusted depending on the nature of discontinuity of the edge profile. The model is based on the biological evidences indicating the possibility that human brain may be endowed with the ability to perform Fourier decomposition of visual images into its various components of spatial frequencies. It may be shown that information obtained from such a Fourier decomposition may help to measure the strength of contrast (sharpness of discontinuity) in the intensity profile across any possible edge in the natural image. In the present model, it is assumed that the magnitude of surround suppression in an excitatory–inhibitory receptive field is dependent on the sharpness of discontinuity. The suppression is strong when the edge contrast is poor, while it becomes weaker as the edge contrast is high. At a biphasic edge, the surround suppression is vanishingly small. Natural images collected from benchmark databases are used to evaluate the efficiency and robustness of the proposed model for the detection of edges. The result shows that the edge maps generated through the proposed model are at par, if not more effective as compared to the classical edge detectors like Canny. The performance of the proposed model is also compared with a number of recently proposed alternative adaptive models for edge detection.
... Edge detection is a long-standing topic dating back to work [17] in 1959. Since then, extensive literature has emerged about edge detection. ...
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Multi-scale representation plays a critical role in the field of edge detection. However, most of the existing research focuses on one of two aspects: fast training and accurate testing. In this paper, we propose a novel multi-scale method to resolve the balance between them. Specifically, according to multi-stream structures and the image pyramid principle, we construct a down-sampling pyramid network and a lightweight up-sampling pyramid network to enrich the multi-scale representation from the encoder and decoder, respectively. Next, these two pyramid networks and a backbone network constitute our overall architecture, a bi-directional pyramid network (BDP-Net). Extensive experiments show that compared with the state-of-the-art model, our method could improve the training speed by about one time while retaining a similar test accuracy. Especially, under the single-scale test, our approach also reaches human perception (F1 score of 0.803) on the BSDS500 database.
... The performance of the edge detection algorithm directly affects the precision of extracted object contours and the performance of the system. In 1959, Julesz [3] was the first to discuss edge detection; later, in 1965, Roberts [4] began to systematically study edge detection. After nearly 60 years of research, many different edge detection methods have been designed, and each has its own characteristics and limitations. ...
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The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator’s dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.
... In industry, the image edge detection of virtual display technology is related to the accuracy of industrial production [21]. In 1959, Julez has already mentioned the edge detection technology Julez, [13], but he didn't conduct the systematic research, while the earliest systematic study of edge detection was conducted by Roberts [20] in 1965, from then on, edge detection has gradually coming into our view, and has been studied by many experts and scholars at home and abroad. However, until now, there is still no perfect algorithm that can fully meet the requirements of people, which makes many experts and scholars still study this topic, and new theories and methods have been continuously put forward, which is still the reason why edge detection is still concerned. ...
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Virtual reality (VR) is based on graphics, images and sensors, and its simulation environment is a real-time and dynamic three dimensions realistic image generated by computer. The detection and extraction method of image edge and contour features is one of the research contents and hotspots in image detection, processing and analysis. Based on the traditional edge detection algorithm, in this paper a dynamic weight edge detection method based on multi-operator is proposed, an image edge detection system based on VR is developed, and the proposed algorithm is compared with the traditional method from the aspects of continuity, smoothness, edge width, positioning accuracy and system performance of the edge. The results indicate that this algorithm is the optimization and supplement of the traditional edge detection algorithm, has certain advantages in continuity and positioning accuracy, and can achieve rapid detection of VR image edge, which provides a theoretical basis for the research of VR image processing in the future.
... To put both problems face to face, we can say that edge detection is an extraction of features appearing in the image, while segmentation provides a global interpretation of it. People have been using 2D edge detection for years (see [Jul59]) for many tasks. For example, to obtain a visually appealing "primal sketch" [MH80,HWL83] of a picture, to reduce the amount of information present in an image, to get a manageable list of "features" to perform registration [AF87, HLF + 97, Bro92] of two images, or shape matching [MAK96,LMMM03]; and finally as a first step towards the segmentation of the image into regions. ...
... Therefore, edge detection and extraction are of great importance to identify and understand the interface information of a multiphase flow image. Since the concept of edge detection was introduced by Julez [6], many edge detection methods have been proposed during last half century, such as the Laplacian operator, Roberts operator, Sobel operator, Prewitt operator, Kirsch operator, Marr operator, Canny operator, and so on [5]. All these operators have no " automatic zoom " function and cannot show the edges of an image in different scales, which is very important to many fields including multiphase flows. ...
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Diese für ein Handbuch ungewöhnliche Darstellung der Röntgenstereoskopie möge damit entschuldigt werden, daß diese Methode immer noch nicht zu den gebräuchlichen in der Röntgendiagnostik gehört. Ein gewissenhaftes Referat würde nur historische Tatsachen, konstruktive Lösungen und Apparate aufzählen, die nicht mehr „auf dem Markte“sind.
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We propose a new edge detector for 3D gray-scale images, extending the 2D edge detector of Desolneux et al. (J. Math. Imaging Vis. 14(3):271–284, 2001). While the edges of a planar image are pieces of curve, the edges of a volumetric image are pieces of surface, which are more delicate to manage. The proposed edge detector works by selecting those pieces of level surface which are well-contrasted according to a statistical test, called Helmholtz principle. As it is infeasible to treat all the possible pieces of each level surface, we restrict the search to the regions that result of optimizing the Mumford-Shah functional of the gradient over the surface, throughout all scales. We assert that this selection device results in a good edge detector for a wide class of images, including several types of medical images from X-ray computed tomography and magnetic resonance.
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Low-level preattentive vision processing is of special interest since it seems the logical starting point of all vision processing. Exploration of the human visual processing system at this level is, however, extremely difficult, but can be facilitated by the use of stroboscopic presentation of sequences of random-dot stereograms, which contain only local spatial and temporal information and therefore limit the processing of these images to the low level. Four experiments are described in which such sequences were used to explore the relationships between various cues (optical flow, stereo disparity, and accretion and deletion of image points) at the low level. To study these relationships in more depth, especially the resolution of conflicting information among the cues, some of the image sequences presented information not usually encountered in 'natural' scenes. The results indicate that the processing of these cues is undertaken as a set of cooperative processes.
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The television game “Concentration” requires that a contestant ask for the sequential removal of blocks covering a diagrammed slogan. After each block is removed, he has the opportunity to guess what the rebus says. This is a form of entertainment that relates to the problem of redundancy of pictorial information. Described here is research on methods for computing redundancy of pictures and machine techniques for measurement of information content of graphics.
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This article develops a neural model of how sharp disparity tuning can arise through experience-dependent development of cortical complex cells. This learning process clarifies how complex cells can binocularly match left and right eye image features with the same contrast polarity, yet also pool signals with opposite contrast polarities. Antagonistic rebounds between LGN ON and OFF cells and cortical simple cells sensitive to opposite contrast polarities enable anticorrelated simple cells to learn to activate a shared set of complex cells. Feedback from binocularly tuned cortical cells to monocular LGN cells is proposed to carry out a matching process that dynamically stabilizes the learning process. This feedback represents a type of matching process that is elaborated at higher visual processing areas into a volitionally controllable type of attention. We show stable learning when both of these properties hold. Learning adjusts the initially coarsely tuned disparity preference to match the disparities present in the environment, and the tuning width decreases to yield high disparity selectivity, which enables the model to quickly detect image disparities. Learning is impaired in the absence of either antagonistic rebounds or corticogeniculate feedback. The model also helps to explain psychophysical and neurobiological data about adult 3-D vision.
Conference Paper
Full-text available
A closed-form single-shot stereo disparity estimation algorithm is proposed that can compute multiple disparities due to transparency directly from signal differences and variations on epipolar lines of a binocular image pair. The transparent stereo constraint equations have been derived by using a novel mathematical technique, the principle of superposition. A computationally tractable single-shot algorithm is derived by using the first-order approximation of the constraint equations with respect to disparities. The algorithm can compute multiple disparities from only two images, in contrast to the previous algorithms for motion transparency, which needed at least n +1 frames for n simultaneous motion estimates. The derived algorithm can be viewed as the SSD (sum of squared differences) for signal matching extended to deal with multiple disparities. However, the constraints are not dedicated solely to the SSD method and several other implementations are possible
Conference Paper
Exploration of human, low-level, pre-attentive vision processing is facilitated using stroboscopic presentation of sequences of random dot stereograms, which contain only local spatial and temporal information thereby limiting the processing of these images to low level. The paper describes four demonstrations that utilize these sequences to explore the relationships of various cues, i.e. optical flow, stereo disparity, and occlusion, at the low level. To better study these relationships, especially the resolution of conflicting information among the cues, some of the image sequences present information not generally encountered in `natural' scenes. Dominance of the stereo cue and the complex relationship between optical flow cues and disparity cues are well demonstrated
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Visual discrimination experiments were conducted using unfamiliar displays generated by a digital computer. The displays contained two side-by-side fields with different statistical, topological or heuristic properties. Discrimination was defined as that spontaneous visual process which gives the immediate impression of two distinct fields. The condition for such discrimination was found to be based primarily on clusters or lines formed by proximate points of uniform brightness. A similar rule of connectivity with hue replacing brightness was obtained by using varicolored dots of equal subjective brightness. The limitations in discriminating complex line structures were also investigated.
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A digital coding and its application to speech transmission is described. The coder determines the amplitudes and times of sucessive extremes (relative maxima and minima) of the signal. This information is decoded at the receiver by interpolating a function between extremes so as to connect them smoothly and preserve the extremes of the original signal in the reconstructed wave, Thus, the coding is a nonlinear sampling technique. It is related to clipped speech encoding which effectively transmits only the times of the extremes. The properties of the coding for speech signals have been studied by digital simulation on an IBM 704 computer. Information rate, statistics of the extremes data, and quality of the resulting signal have been evaluated. The buffer size necessary to receive the randomly occurring data and transmit at a constant rate was measured.
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Digital simulation is a powerful tool in uncovering the basic properties of new or proposed communications principles, particularly those involving coding of visual or auditory information. Operating on digitalized speech or pictorial signals, a stored program computer can perform processing equivalent to any coding. The output signals so produced can then be made available for subjective evaluation, thereby removing the necessity for premature instrumentation to produce samples for viewing or listening. This technique owes its efficacy to 1) the availability of computers fast enough to accomplish the processing in a reasonable time scale, 2) the existence of high quality translators to implement the flow of continuous signals in and out of the computer, and 3) the creation of compiling programs which allow uninitiated investigators almost immediate access to computer facilities, and which keep programming effort low. Simulation is assuming an increasing role in communications research.
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Published papers and reports dealing with data compression, picture properties and models, picture coding and transmission, image enhancement, and human visual information processing are listed.
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This bibliography contains books, papers, and articles on the subject of television (TV) bandwidth reduction. Also included is related information on the processing and coding of video signals, and the subjective evaluation of TV images.
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Closed circuit television operations have been able to make use of the vidicon camera to advantage and its use is suggested for particular broadcast applications. Where high illumination and flat lighting is available, the vidicon is preferable to the image orthicon as a pickup device because of its greater economy, stability, ruggedness, simplicity of circuitry and small size, and excellent picture quality.
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The principal psychophysical requirements of the human eye are satisfied by a television frame frequency of about 1 or 2 c/s. To avoid flicker with such low-frame rates, a random dot scan is employed in conjunction with long-persistence phosphors. Experiments show that 15 per cent dot flicker is tolerable. Ten systems are discussed, including a two-million-element picture with 4-Mc/s bandwidth; tape recording at 15 in/s; continuous relaying of satellite pictures; short-wave transmission; phonevision; and very low-frame rate applications that use a scan conversion tube.
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The correlation present in a signal makes possible the prediction of the future of the signal in terms of the past and present. If the method used for prediction makes full use of the entire pertinent past, then the error signal—the difference between the actual and the predicted signal—will be a completely random wave of lower power than the original signal but containing all the information of the original. One method of prediction, which does not make full use of the past, bid which is nevertheless remarkably effective with certain signals and also appealing because of its relative simplicity, is linear prediction. Here the prediction for the next signal sample is simply the sum of previous signal samples each multiplied by an appropriate weighting factor. The best values for the weighting coefficients depend upon the statistics of the signal, bid once they have been determined the prediction may be done with relatively simple apparatus. This paper describes the apparatus used for some experiments on linear prediction of television signals, and describes the results obtained to date.
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This paper reviews briefly a few of the simpler aspects of communication theory, especially those parts which relate, to the information rate of and channel capacity required for sampled, quantized messages. Two methods are then discussed, whereby such messages can be converted to a “reduced” form in which the successive samples are more nearly independent and for which the simple amplitude distribution is more peaked than in the original message. This reduced signal can then be encoded into binary digits with good efficiency using a Shannon-Fano code on a symbol-by-symbol (or pair-by-pair) basis. The usual inefficiency which results from ignoring the correlation between message segments is lessened because this correlation is less in the reduced message.
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Transmission by pulse code modulation presents inviting possibilities in the field of television in that information may be relayed by many repeater stations without deterioration. In a PCM system, the information signal is periodically sampled and its instantaneous amplitude described by a group of pulses according to a pre-set code. These pulse groups occur at the sampling rate and constitute the transmitted signal. In this process an operation known as amplitude quantization is required. This paper will include a discussion of time sampling, amplitude quantization, binary coding and decoding of a television signal. The operation of the equipment used to perform these functions is described. The results obtained with an experimental system for different numbers of digits (i.e., maximum number of pulses per group) from one to five are illustrated by photographs. The television signal used in these tests was obtained from a special low noise film scanner. As was expected, the number of digits required depends upon the amount of noise in the test signal.
A Computer Simulation Chain for Re-search on Picture Coding, LR.E.-Wescon Cony
  • R E Graham
  • J L Kelly
  • Jr
Graham, R. E. and Kelly, J. L., Jr., A Computer Simulation Chain for Re-search on Picture Coding, LR.E.-Wescon Cony. Rec. (Computer Appli-cations), August 1958, p. 41.