A content-based image retrieval scheme based on scale-space theory is proposed. Instead of considering all scales for image retrieval, the proposed algorithm utilizes a modified histogram intersection method to compute the relative scale between a query image and a candidate image. The proposed method has been applied to various images and the performance improvement has been verified.

Precise estimation of coronary arteries from computed tomography angiography (CTA) data is one of the challenging problems. This study focuses on automatic delineation of coronary arteries from 3D CTA data that may assess the clinicians in identifying the coronary pathologies. In this work, we present a technique that effectively segments the complete coronary arterial tree under the guidance of initial vesselness response without relying on heavily manual operations. The proposed method isolates the coronary arteries with accuracy by using localized statistical energy model in two directions provided with an automated seed which ensures an optimal segmentation of the coronaries. The detection of seed is carried out by analyzing the shape information of the coronary arteries in three successive cross-sections. To demonstrate the efficiency of the proposed algorithm, the obtained results are compared with the reference data provided by Rotterdam framework for lumen segmentation and the level-set active contour based method proposed by Lankton et al. Results reveal that the proposed method performs better in terms of leakages and accuracy in completeness of the coronary arterial tree.

In this paper, an algorithm for anchor frame detection in news video is proposed, which consists of four steps. First, the cumulative histogram method is used to detect shot boundaries in order to segment a news video into video shots. Second, skin color information is used to detect face regions in each video shot. Third, color information of upper body regions is used to extract anchor object. Then, a graph-theoretic cluster analysis algorithm is utilized to classify the news video into anchor-person shots and non-anchor shots. Experimental results have shown the effectiveness of the proposed algorithm.

In this paper, efficient algorithms for content-based video retrieval using motion information are proposed. We describe algorithms for temporal scale invariant retrieval using Distance transformation and temporal scale absolute retrieval using Motion Retrieval Code. The effectiveness of the proposed algorithms has been verified by experimental results.