Letter from the incoming Editor-in-Chief

[No abstract available

Honoring Murat Kunt

[No abstract available

Projections Onto Convex Sets (POCS) Based Optimization by Lifting

Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in R^N the corresponding set is a convex set in R^(N+1). The iterative optimization approach starts with an arbitrary initial estimate in R^(N+1) and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp, p<1 can be handled by using the supporting hyperplane concept

Special issue on microscopic image processing

[No abstract available

Co-difference based object tracking algorithm for infrared videos

This paper presents a novel infrared (IR) object tracking algorithm based on the co-difference matrix. Extraction of co-difference features is similar to the well known covariance method except that the vector product operator is redefined in a multiplication-free manner. The new operator yields a computationally efficient implementation for real time object tracking applications. Experiments on an extensive set of IR image sequences indicate that the new method performs better than covariance tracking and other tracking algorithms without requiring any multiplication operations. © 2016 IEEE

Object tracking under illumination variations using 2D-cepstrum characteristics of the target

Most video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In this paper, a 2D-cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target object provides robustness to varying illumination conditions. Another contribution of the paper is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method. ©2010 IEEE

Man-made object classification in SAR images using 2-D cepstrum

In this paper, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. The new method is based on the two-dimensional (2-D) real cepstrum. This novel 2-D cepstrum method is compared with principal component analysis (PCA) method by testing over the MSTAR image database. The extracted features are classified using Support Vector Machine (SVM). We demonstrate that discrimination of natural background (clutter) and man-made objects (metal objects) in SAR imagery is possible using the 2-D cepstrum feature parameters. In addition, the computational cost of the cepstrum method is lower than the PCA method. Experimental results are presented. ©2009 IEEE

Shadow detection using 2D cepstrum

Shadows constitute a problem in many moving object detection and tracking algorithms in video. Usually, moving shadow regions lead to larger regions for detected objects. Shadow pixels have almost the same chromaticity as the original background pixels but they only have lower brightness values. Shadow regions usually retain the underlying texture, surface pattern, and color value. Therefore, a shadow pixel can be represented as a.x where x is the actual background color vector in 3-D RGB color space and a is a positive real number less than 1. In this paper, a shadow detection method based on two-dimensional (2-D) cepstrum is proposed. © 2009 SPIE

HMM based method for dynamic texture detection

A method for detection of dynamic textures in video is proposed. It is observed that the motion vectors of most of the dynamic textures (e.g. sea waves, swaying tree leaves and branches in the wind, etc.) exhibit random motion. On the other hand, regular motion of ordinary video objects has well-defined directions. In this paper, motion vectors of moving objects are estimated and tracked based on a minimum distance based metric. The direction of the motion vectors are then quantized to define two threestate Markov models corresponding to dynamic textures and ordinary moving objects with consistent directions. Hidden Markov Models (HMMs) are used to classify the moving objects in the final step of the algorithm

Breathing detection based on the topological features of IR sensor and accelerometer signals

This paper describes a non-contact breathing detection system using a pyro-electric infrared (PIR) sensor and an accelerometer. The multi-sensor system can be used to detect the respiratory disorders. A PIR sensor is placed onto a stand near a bed and an accelerometer is placed on the mattress. We recently developed a PIR sensor which is capable of producing 1-D time-varying signals corresponding to the motions in its field of view. The PIR sensor signal due to the thoracic movements turns out to be an almost periodic signal. Similarly, the accelerometer produces an almost periodic signal in response to vibrations in bed. Sensor signals are processed using a topological approach. Point clouds are constructed from the delay-coordinate embedding of the time series sensor data first. Then, periodic structures in the point clouds are detected using persistent homology. The sensors, with the proposed method, complement each other to produce more accurate decisions in different lying positions. © 2016 IEEE