Work place: UEB INSA IETR Département Image et Automatique, 35708 Rennes, France
Research Interests: Image Processing, Image Manipulation, Image Compression, Pattern Recognition
Kidiyo KPALMA received his PhD in Image Processing from the National Institute of Applied Sciences of Rennes (INSA) in 1992 and his HDR (Habilitation à diriger des recherches) in Signal processing and Telecommunications from the University of Rennes 1 in 2009 in France. He is currently Full Professor at INSA where he teaches signals and systems, signal processing and DSP. As a member of the Image and Automatic department of the Institute of Electronics and Telecommunications of Rennes (IETR), his research interests are image analysis, pattern recognition, image segmentation, image fusion and remote sensing.
DOI: https://doi.org/10.5815/ijigsp.2016.01.01, Pub. Date: 8 Jan. 2016
In this paper, we present an efficient region-based image retrieval method, which uses multi-features color, texture and edge descriptors. In contrast to recent image retrieval methods, which use discrete wavelet transform (DWT), we propose using shape adaptive discrete wavelet transform (SA-DWT). The advantage of this method is that the number of coefficients after transformation is identical to the number of pixels in the original region. Since image data is often stored in compressed formats: JPEG 2000, MPEG 4…; constructing image histograms directly in the compressed domain, allows accelerating the retrieval operation time, and reducing computing complexities. Moreover, SA-DWT represents the best way to exploit the coefficients characteristics, and properties such as the correlation. Characterizing image regions without any conversion or modification is first addressed. Using edge descriptor to complement image region characterizing is then introduced. Experimental results show that the proposed method outperforms content based image retrieval methods and recent region based image retrieval methods.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2015.08.01, Pub. Date: 8 Jul. 2015
Multi-object tracking is a challenging task, especially when the persistence of the identity of objects is required. In this paper, we propose an approach based on the detection and the recognition. To detect the moving objects, a background subtraction is employed. To solve the recognition problem, a classification system based on sparse representation is used. With an online dictionary learning, each detected object is classified according to the obtained sparse solution. Each column of the used dictionary contains a descriptor representing an object. Our main contribution is the representation of the moving object with a descriptor derived from a novel representation of its 2-D position and a histogram-based feature, improved by using the silhouette of this object. Experimental results show that the approach proposed for describing moving objects, combined with the classification system based on sparse representation provides a robust multi-object tracker in videos involving occlusions and illumination changes.[...] Read more.
Subscribe to receive issue release notifications and newsletters from MECS Press journals