MAPS: midline analysis and propagation of segmentation

Scenic word images undergo degradations due to motion blur, uneven illumination, shadows and defocussing, which lead to difficulty in segmentation. As a result, the recognition results reported on the scenic word image datasets of ICDAR have been low. We introduce a novel technique, where we choose the middle row of the image as a sub-image and segment it first. Then, the labels from this segmented sub-image are used to propagate labels to other pixels in the image. This approach, which is unique and distinct from the existing methods, results in improved segmentation. Bayesian classification and Max-flow methods have been independently used for label propagation. This midline based approach limits the impact of degradations that happens to the image. The segmented text image is recognized using the trial version of Omnipage OCR. We have tested our method on ICDAR 2003 and ICDAR 2011 datasets. Our word recognition results of 64.5% and 71.6% are better than those of methods in the literature and also methods that competed in the Robust reading competition. Our method makes an implicit assumption that degradation is not present in the middle row.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.

Lizard Wears Shades. A Spectacled Sphenomorphus (Squamata: Scincidae), from the Sacred Forests of Mawphlang, Meghalaya, North-east India

A new species of lygosomatine scincid lizard is described from the sacred forests of Mawphlang, in Meghalaya, northeastern India. Sphenomorphus apalpebratus sp. nov. possesses a spectacle or brille, an unusual feature within the Scincidae, and a first for the paraphyletic genus Sphenomorphus. The new species is compared with other members of the genus to which it is here assigned, as well as to members of the lygosomatine genera Lipinia and Scincella from mainland India, the Andaman and Nicobar Islands, and south-east Asia, to which it also bears resemblance. The new taxon is diagnosable in exhibiting the following combination of characters: small body size (SVL to 42.0 mm); moveable eyelids absent; auricular opening scaleless, situated in a shallow depression; dorsal scales show a line of demarcation along posterior edge of ventral pes; midbody scale rows 27-28; longitudinal scale rows between parietals and base of tail 62-64; lamellae under toe IV 8-9; supraoculars five; supralabials 5-6; infralabials 4-5; subcaudals 92; and dorsum golden brown, except at dorsal margin of lateral line, which is lighter, with four faintly spotted lines, two along each side of vertebral row of scales, that extend to tail base. The new species differs from its congeners in the lack of moveable eyelids, a character shared with several distantly related scincid genera.