Statistical-learning-based object detection is an important topic in computer vision. It learns
visual representation from annotated exemplars to identify semantic defined objects in …

In this review, image clustering problem is discussed starting from global learning based
clustering approaches such as Kmeans to the recent challenges in this domain. In global …

Accurate malignancy level classification of lung nodules can reduce lung cancer mortality
rate effectively. In this study, we present a fast and efficient CAD system to improve the …

High-resolution remote sensing images (RSIs) have been adopted in satellites, RSIs
processing in satellites will enable new multimedia applications, such as situational …

The aim of this paper is to give an overview of state-of-the-art human detection methods,
classify them into reasonable categories and listing new trends in this field. Human detection …

Object segmentation and classification in the video sequence is a classical and critical
problem that is constantly addressed due to vast real-time applications such as autonomous …

Nowadays, image processing is an interesting research area due to the growth of the
communication technologies. Matching problem, which consists of localizing one texture in …

In this paper, a new framework for detecting text from webpage and email images is
presented. The original image is split into multiple layer images based on the maximum …

In uncontrolled video surveillance environments, performing efficient foreground
segmentation is very challenging. In order to improve robustness and accuracy of object …

Manifold learning based image clustering models are usually employed at local level to deal
with images sampled from nonlinear manifold. Multimode patterns in image data matrices …