Anass Dr. Belcaid

Associate Professor

University Cadi Ayyad

email: anass.belcaid@gmail.com

I’m an Applied Mathematics and Computer Science associate Professor at National School of Applied Sciences - Safi. Prior to that I was an associate Professor in the School of Digital Engineering and Artificial Intelligence at the Euromed university. I finished my PhD in 2018 at ENSAM School in the laboratory of Professor M.Douimi. I got my master’s degree in grid computing and asynchronous algorithms at Faculty of Science Meknès. More information are on Personal curriculum

Research Interests

I’m currently working on edge detection in computer vision using a combination of convolution networks and Residual Dense networks.

I’m also starting a new project to investigate maximum entropy methods in reinforcement learning and their combination with hierarchical models using complex architecture than the actor/critic.

Prospective Students

I’m looking for motivated and visiting students. For a list of available projects check Available projects.

selected publications

EnVar

Constrained energy variation for change point detection

Belcaid, A., and Belkbir, H.

Multidimensional Systems and Signal Processing 2021

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The problem of change point detection can be solved either by online methods, based on a discrepancy measure, or by offline methods. The former tries to detect the change points one by one with a sliding window and leads to a lower computational time but are more sensitive to noise. Conversely, offline methods consider the entire data to detect all the change points which make them more robust against the noise but at a price of higher computational cost. In this paper, we propose an operational search method that combines the benefits of both approaches with the double aim to get higher noise resistance while keeping a blazingly fast time. The search method slides over the edges of the signal to determines their state by considering a global constrained energy. Thanks to the calculus of variation, the computation of this energy is reduced to the estimation of the effective jump for each edge. We study the performance and accuracy of our energy variation method to detect the change points in synthetic and real-world examples. The results compare favorably against state of the art algorithm in terms of speed and accuracy.
OCPC

A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy

Belcaid, A., and Douimi, M.

International Journal on Artificial Intelligence Tools 2020

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In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF) representing the signal and its noisy observation. For each window, we define and solve the local energy minimization problem to deduce the gradient on each edge of the MRF graph. The gradient is then processed by an activation function to filter the weak features and produce the final jumps. We demonstrate the effectiveness of our method by comparing its running time and several detection metrics with state of the art algorithms.
recDps

Recursive reconstruction of piecewise constant signals by minimization of an energy function

Belcaid, A., M. Douimi, , and Fihri, A.

Inverse Problems & Imaging 2018

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The problem of denoising piecewise constant signals while preserving their jumps is a challenging problem that arises in many scientific areas. Several denoising algorithms exist such as total variation, convex relaxation, Markov random fields models, etc. The DPS algorithm is a combinatorial algorithm that excels the classical GNC in term of speed and SNR resistance. However, its running time slows down considerably for large signals. The main reason for this bottleneck is the size and the number of linear systems that need to be solved. We develop a recursive implementation of the DPS algorithm that uses the conditional independence, created by a confirmed discontinuity between two parts, to separate the reconstruction process of each part. Additionally, we propose an accelerated Cholesky solver which reduces the computational cost and memory usage. We evaluate the new implementation on a set of synthetic and real world examples to compare the quality of our solver. The results show a significant speed up, especially with a higher number of discontinuities.