International Journal of Image and Graphics

Storyboard consisting of key-frames is a popular format of video summarization as it helps in efficient indexing, browsing and partial or complete retrieval of video. In this paper, we have presented a size constrained storyboard generation scheme. Given the shots i.e. the output of the video segmentation process, the method has two major steps: extraction of appropriate key-frame(s) from each shot and finally, selection of a specified number of key-frames from the set thus obtained. The set of selected key-frames should retain the variation in visual content originally possessed by the video. The number of key-frames or representative frames in a shot may vary depending on the variation in its visual content. Thus, automatic selection of suitable number of representative frames from a shot still remains a challenge. In this work, we propose a novel scheme for detecting the sub-shots, having consistent visual content, from a shot using Wald–Wolfowitz runs test. Then from each sub-shot a frame rendering the highest fidelity is extracted as key-frame. Finally, a spanning tree based novel method is proposed to select a subset of key-frames having specific cardinality. Chronological arrangement of such frames generates the size constrained storyboard. Experimental result and comparative study show that the scheme works satisfactorily for a wide variety of shots. Moreover, the proposed technique rectifies mis-detection error, if any, incurred in video segmentation process. Similarly, though not implemented, the proposed hypothesis test has ability to rectify the false-alarm in shot detection if it is applied on pair of adjacent shots.

This paper deals with the problem of detecting "weak symmetry" in a polygon, which is a special bijective and continuous mapping between the vertices of the given polygon. An application of this work is the automatic reconstruction of 3D polygons symmetric with respect to a plane from free-hand sketches of weakly-symmetric 2D polygons. We formalize the weak-symmetry notion and highlight its many properties which lead to an algorithm detecting it. The closest research work to the proposed approach is the detection of skewed symmetry. Skewed symmetry detection deals only with reconstruction of planar mirror-symmetric 3D polygons while our method is able to identify symmetry in projections of planar as well as nonplanar mirror-symmetric 3D polygons.

This paper proposes a method to remove JPEG noise artifacts from frame sequences. Using extensive experimental results we show how an online system with periodic noise estimation functionality can estimate the real frame noise even if the images are in JPEG format. We present the mathematical basis of the methodology and show in real content that we can have reliable measurements. We also present the results obtained on a real network camera and show that our method can provide a much better estimation of the noise standard deviation compared to common practice but comparable inter-channel and spatial intra-channel correlation estimates. We also provide some guidelines for capturing datasets necessary to apply computer vision tasks. Our approach exploits the well known stochastic linearization phenomenon which we prove that is present in our case.

In this paper, we have introduced a new method for watermark (logo) extraction from distorted watermarked images. The method is based on combined discrete wavelet transform (DWT) and dynamic stochastic resonance (DSR). In this method, the image property such as variance corresponding to the DWT coefficients of the image is tuned with the dynamic stochastic resonance parameters which causes resonance to the DWT coefficients. That is, the signal amplitude enhances and noise amplitude degraded in the DWT coefficients. This approach extracts hided logo from the distorted watermarked image which is almost very similar to the original logo. The experimental results have been compared with the existing techniques and were found to be superior.

This paper presents a listless variant of modified wavelet block tree coding (MLBTC) algorithm. Wavelet block-tree coding (WBTC) improves the image compression performance of set partitioning in hierarchical trees (SPIHT) at lower rates by efficiently encoding both inter- and intra-scale correlations. The use of auxiliary lists makes WBTC undesirable for hardware implementation as it needs a lot of memory management due to exponential increase of nodes on each pass. The proposed coder named as hierarchical listless block-tree DTT (HLBT_DTT) that combines discrete Tchebichef transform (DTT) with MLBTC exhibits compression performance significantly higher than most of the DCT-based embedded coders and comparable with JPEG 2000, especially at lower bit rates. Further, HLBT_DTT requires only 10% of memory than that of DCT-based SPIHT/WBTC coders.

The paper reveals the analysis of the compression quality of true color medical images of echocardiogram (ECHO), X-radiation (X-ray) and computed tomography (CT) and further a comparison of compressed biomedical images of various sizes using two lossy compression techniques, set partitioning in hierarchical trees (SPIHT) and discrete cosine transform (DCT) to the original image is carried out. The study also evaluates the results after analyzing various objective parameters associated with the image. The objective of this analysis is to exhibits the effect of compression ratio on absolute average difference (AAD), cross correlation (CC), image fidelity (IF), mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity index measurement (SSIM) of the compressed image by SPIHT and DCT compression techniques. The results signify that the quality of the compressed image depends on resolution of the underlying structure where CT is found to be better than other image modalities. The X-ray compression results are equivalent by both the techniques. The compression results for large size biomedical images by SPIHT signifies that ECHO having comparable results to CT and X-ray while their DCT results are substandard. The compression results for comparatively smaller images of ECHO are not as good as X-ray and CT by both the compression techniques. The quality measurement of the compressed image has been designed using MATLAB.

A simple edge-based preprocessing scheme is proposed in this paper for contrast enhancement of digital mammogram images while preserving the edges more accurately. This proposed method has three steps: (i) initially the breast region is segmented from the mammogram images by removing the film artifacts, (ii) the pectoral muscle region is identified and excluded from the breast region using a novel adaptive thresholding method, and (iii) an Improved Watershed Segmentation (IWS) is applied to segment the breast profile, and each region is enhanced with simple histogram equalization. The segmentation is performed in order to achieve adaptive contrast enhancement. The performance of this proposed pectoral removal method is analyzed with two measures: Hausdorff Distance (HD) and Mean of Absolute Error Distance (MAED), and the proposed contrast enhancement approach is been analyzed with the five diverse parameters along with the classification accuracy. The experiments and results show the potential performance of our proposed algorithm over the existing approaches with optimum results on all the performance measure and the classification performance is been evaluated with a hybrid neural network, our proposed method proves the better performance with the achievement of 92% accuracy.