Ibrahim Kajo

• PhD in Electrical and Electronic Engineering
• Researcher at University of Technology of Belfort-Montbéliard

The superior performance introduced by deep learning approaches in removing atmospheric particles such as snow and rain from a single image; favors their usage over classical ones. However, deep learning-based approaches still suffer from challenges related to the particle appearance characteristics such as size, type, and transparency. Furthermore...

Single-image haze removal is an essential preprocessing phase in many object detection and segmentation approaches. Recently, end-to-end deep learning-based approaches have dominated the field of single-image dehazing because of their superiority in recovering clear images corrupted by different types of degradation. However, training an effective...

Background initialization is an essential step for both hand-crafted and deep learning foreground segmentation approaches. In this paper, we propose a low-rank approximation algorithm that effectively handles the challenge caused by Stationary Foreground Objects (SFOs) on both offline and online bases. The proposed algorithm employs different incre...

Image enhancement has recently gained considerable attention owing to its benefits in cleaning input images before final processing and, thus, helping decision systems. The outstanding success of generative adversarial networks has been exploited in image enhancement to handle various image generation challenges, and complex image enhancers incorpo...

In recent years, there has been a growing interest in the use of Generative Adversarial Networks (GANs). Thanks to their outstanding performance in image translation and generation, they play an increasingly important role in computer vision applications. Most approaches based on GAN focus on proposing task-specific auxiliary modules or loss functi...

Foreground segmentation is an essential processing phase in several change detection-based applications. Classical foreground segmentation is highly dependent on the accuracy of the estimated background model and the procedures followed to subtract such model from the original frame. Obtaining good foreground masks via background subtraction remain...

Frame interpolation and synthesis are growing topics in the field of computer vision. Hence, these topics gained more attention recently where several deep-learning architectures were proposed to enhance the quality of the synthesized frames. In this paper, an efficient handcrafted deep approach is proposed for better frame synthesis. The proposed...

Eye blink detection is a challenging problem that many researchers are working on because it has the potential to solve many facial analysis tasks, such as face anti-spoofing, driver drowsiness detection, and some health disorders. There have been few attempts to detect blinking in the wild scenario, while most of the work has been done under contr...

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposi...

The complexity of a scene in addition to the need for real-time processing are the main challenges that face any background/foreground separation approach for maritime environment. Recent studies on Low-rank and Sparse Separation (LSS) achieved good performance when compared to traditional background subtraction techniques in segregating the fore...

The background Initialization (BI) problem has attracted the attention of researchers in different image/video processing fields. Recently, a tensor-based technique called spatiotemporal slice-based singular value decomposition (SS-SVD) has been proposed for background initialization. SS-SVD applies the SVD on the tensor slices and estimates the ba...

The periodicity of an object is one of its most important visual characteristics. Recently, several low-rank/sparse matrix decomposition techniques have indicated that a relationship exists between the frequency components of the motion matrix and its decomposition components. This relationship was mostly identified based on empirical evidence with...

In tasks such as abandoned luggage detection and stopped car detection, Stationary Foreground Objects (SFOs) need to be detected and properly classified in real time. Different methods have been proposed to detect SFOs, but they are mainly focused on certain types of objects. In this paper, an incremental singular value decomposition-based method i...

Extracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular-value decomposition (SVD) of spatiotemporally extracted slices fr...

For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. However, algorithms based on optical flow and background subtraction have numerous limitations such as the complexity of the computation in the pre...

Optical flow technique is one of the significant motion estimation techniques. Due to its importance, several optical flow technique have been used in order to estimate the velocity and the direction of the pedestrians in the crowded scenes. This paper presents an overview of the optical flow methods that used mainly for pedestrian and crowd motion...

Velocity and direction estimation plays an important role in crowd analytic and behavior recognition. This paper presents an overview of the literature published for motion detection and estimation techniques. The work particularly focuses on optical flow techniques such as Lucas & Kanade and Horn & Schunck methods which describe the direction and...

In this paper, a novel motion estimation method is proposed in order to enhance the block based matching. The enhancement is achieved by eliminating the number of search points, which in turn will reduce the computation complexity of any block based matching method. Relying on the social force model, a predicted direction of the motion vectors can...

The optical flow describes the direction and time rate of pixels in a time sequence of two consequent images. A two-dimensional velocity vector, carrying information on the direction and the velocity of motion is assigned to each pixel in a given place in the picture. This article describes different motion detection methods, gives a brief illustra...