Coupled Depth Learning

In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map has been computed. We express a global (coarse) depth map of an image as a linear combination of a depth basis learned from training examples. The depth basis captures spatial and statistical regularities and reduces the problem of global depth estimation to the task of predicting the input-specific coefficients in the linear combination. This is formulated as a regression problem from a holistic representation of the image. Crucially, the depth basis and the regression function are {\bf coupled} and jointly optimized by our learning scheme. We demonstrate that this results in a significant improvement in accuracy compared to direct regression of depth pixel values or approaches learning the depth basis disjointly from the regression function. The global depth estimate is then used as a guidance by a local refinement method that introduces depth details that were not captured at the global level. Experiments on the NYUv2 and KITTI datasets show that our method outperforms the existing state-of-the-art at a considerably lower computational cost for both training and testing.Comment: 10 pages, 3 Figures, 4 Tables with quantitative evaluation

Human papilloma virus 16/18: Fabricator of trouble in oral squamous cell carcinoma

Aim: To find out the association between Human Papilloma Virus (HPV) genotypes 16/18 in Pakistani patients with oral squamous cell carcinoma (OSCC). Methods: DNA from oral rinse of 300 subjects was taken. The subjects included 100 cases with OSCC and 200 controls. Samples were analyzed by both conventional and real time PCR using “HPV consensus Gp5+/Gp6+ and HPV 16, 18 specific primers”. Results: Out of 300 persons, 74/300 (25%) were found to be infected with HPV: “46/100(46%) from cases and 74/200(14%) from controls”. The distribution was: HPV16, 6/300 (8%): 4/100 (9%) from OSCC group and 2/200 (8%) from controls while HPV 18 was 9/300(12%): 5/100(11%) from cases and 4/200(16%) from controls. Out of 300 subjects, 26(35%) were infected by “both HPV 16/18 (23(50%) from cases and 3(12%) from controls”. Persons who were infected with HPV 16&18 had higher chances to develop OSCC as compared to those who didn’t have HPV 16/18 (AOR: 21.4, 95% CI: 5.73 – 80.8). Conclusion: The exposure to high risk strains of Human papilloma virus (16/18) in combination can be fabricotor of trouble (p < 0.001, Adjusted odds ratio; 21.42) in OSCC. Keywords: Human papilloma virus 16/18, Oral squamous cell carcinoma, Real time PCR, Pakista

Im2depth: Scalable exemplar based depth transfer

The rapid increase in number of high quality mobile cameras have opened up an array of new problems in mo-bile vision. Mobile cameras are predominantly monocular and are devoid of any sense of depth, making them heavily reliant on 2D image processing. Understanding 3D struc-ture of scenes being imaged can greatly improve the per-formance of existing vision/graphics techniques. In this regard, recent availability of large scale RGB-D datasets beg for more effective data driven strategies to leverage the scale of data. We propose a depth recovery mechanism ”im2depth”, that is lightweight enough to run on mobile platforms, while leveraging the large scale nature of mod-ern RGB-D datasets. Our key observation is to form a ba-sis (dictionary) over the RGB and depth spaces, and repre-sent depth maps by a sparse linear combination of weights over dictionary elements. Subsequently, a prediction func-tion is estimated between weight vectors in RGB to depth space to recover depth maps from query images. A final su-perpixel post processor aligns depth maps with occlusion boundaries, creating physically plausible results. We con-clude with thorough experimentation with four state of the art depth recovery algorithms, and observe an improvement of over 6.5 percent in shape recovery, and over 10cm reduc-tion in average L1 error. 1