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A deblocking method based on projection onto convex sets (POCS) is proposed to reduce blocking artifacts in compressed images coded by the block discrete cosine transform. The method differs from existing POCS-based methods in three aspects. Firstly, the adjustment of a pixel's intensity is determined by local properties of the pixel. Secondly, three locally adaptive constraint sets are introduced to improve deblocking results. Thirdly, the human visual system modeling and relaxed projections are incorporated to make pixels adjust appropriately. The method is tested on typical images with excellent results. Index Terms--Block discrete cosine transform (BDCT}, blocking artifact, JPEG, MPEG, projection onto convex sets (POCS).

A major problem with traditional skeletonization algorithms is that their results do not always conform to human perceptions since they often contain unwanted artifacts. This paper presents an indirect skeletonization method to reduce these artifacts. The method is based on analyzing regularities and singularities of shapes. A shape is first partitioned into a set of triangles using the constrained Delaunay triangulation technique. Then, regular and singular regions of the shape are identified from the partitioning. Finally, singular regions are stabilized to produce a better result. Experiments show that skeletons obtained from the proposed method closely resemble human perceptions of the underlying shapes. Index Terms--Binary image analysis, constrained Delaunay triangulation, skeletonization, thinning, thinning artifacts.

Colour detection plays an important role for many computer vision-based applications. However, most existing colour detection methods tend to be environment dependent since slight changes of environmental factors such as illumination or shadowing effects could greatly reduce their performances. In this paper, a new colour model is introduced to allow enhanced colour detection from images, even with significantly different lighting conditions and image qualities. The proposed colour model is called the HPBr colour model. It is converted from the RGB colour model and it consists of three colour components, namely, hue (H), purity (P) and brightness (Br). This colour model can be represented in three different geometric shapes: diamond, sphere and cylinder. To assess the effectiveness of the model, two different colour detection methods have been applied onto benchmark images. Experimental results from both methods confirmed that the proposed colour model produced the best colour detection results among existing models. [ABSTRACT FROM AUTHOR]