Scalable Object Recognition Using Hierarchical Quantization with a Vocabulary Tree

An image retrieval technique employing a novel hierarchical feature/descriptor vector quantizer tool—‘vocabulary tree’, of sorts comprising hierarchically organized sets of feature vectors—that effectively partitions feature space in a hierarchical manner, creating a quantized space that is mapped to integer encoding. The computerized implementation of the new technique(s) employs subroutine components, such as: A trainer component of the tool generates a hierarchical quantizer, Q, for application/use in novel image-insertion and image-query stages. The hierarchical quantizer, Q, tool is generated by running k-means on the feature (a/k/a descriptor) space, recursively, on each of a plurality of nodes of a resulting quantization level to ‘split’ each node of each resulting quantization level. Preferably, training of the hierarchical quantizer, Q, is performed in an ‘offline’ fashion

RPNet: an End-to-End Network for Relative Camera Pose Estimation

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation

Novel Perspectives on p53 Function in Neural Stem Cells and Brain Tumors

Malignant glioma is the most common brain tumor in adults and is associated with a very poor prognosis. Mutations in the p53 tumor suppressor gene are frequently detected in gliomas. p53 is well-known for its ability to induce cell cycle arrest, apoptosis, senescence, or differentiation following cellular stress. That the guardian of the genome also controls stem cell self-renewal and suppresses pluripotency adds a novel level of complexity to p53. Exactly how p53 works in order to prevent malignant transformation of cells in the central nervous system remains unclear, and despite being one of the most studied proteins, there is a need to acquire further knowledge about p53 in neural stem cells. Importantly, the characterization of glioma cells with stem-like properties, also known as brain tumor stem cells, has opened up for the development of novel targeted therapies. Here, we give an overview of what is currently known about p53 in brain tumors and neural stem cells. Specifically, we review the literature regarding transformation of adult neural stem cells and, we discuss how the loss of p53 and deregulation of growth factor signaling pathways, such as increased PDGF signaling, lead to brain tumor development. Reactivation of p53 in brain tumor stem cell populations in combination with current treatments for glioma should be further explored and may become a viable future therapeutic approach

MUC1 as a Putative Prognostic Marker for Prostate Cancer

MUC1 is expressed on the apical surface of glandular epithelium. With functions including protection, adhesion and signaling, MUC1 has been implicated in prostate cancer. There are many splice variants, the best characterized of which are MUC1/1 and MUC1/2 which are determined by a SNP (rs4072037, 3506G>A)

Clonality Analysis of Synchronous Lesions of Cervical Carcinoma Based on X Chromosome Inactivation Polymorphism, Human Papillomavirus Type 16 Genome Mutations, and Loss of Heterozygosity

One of the most common forms of carcinoma in women, cervical invasive squamous cell carcinoma (CIC), often coexists with multiple lesions of cervical intraepithelial neoplasia (CIN). CIC and CIN show heterogeneity with respect to both histopathology and biology. To understand the causes, origin, and model of progression of cervical carcinoma, we assessed the clonality of a case with multiple synchronous lesions by analyzing X chromosome inactivation polymorphism, human papillomavirus type 16 (HPV16) sequence variation/mutations, and loss of heterozygosity (LOH). Microdissection was performed on 24 samples from this case, representing the entire lesional situation. The combination of different X chromosome inactivation patterns, two HPV16 point mutations, and LOH at three genomic microsatellite loci, led to the identification of five different “monoclonal” lesions (CIN II, CIN III, and invasive carcinoma nests) and five different “polyclonal” areas (CIN II and normal squamous epithelium). This finding indicated that CIC can originate from multiple precursor cells, from which some clones might progress via multiple steps, namely via CIN II and CIN III, whereas others might develop independently and possibly directly from the carcinoma precursor cells. Our results also supported the view that HPV16 as a “field factor” causes cervical carcinoma, which is probably promoted by the loss of chromosomal material as indicated by the LOH

Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle

The internal calibration of a pinhole camera is given by five parameters that are combined into an upper-triangular $3\times 3$ calibration matrix. If the skew parameter is zero and the aspect ratio is equal to one, then the camera is said to have Euclidean image plane. In this paper, we propose a non-iterative self-calibration algorithm for a camera with Euclidean image plane in case the remaining three internal parameters --- the focal length and the principal point coordinates --- are fixed but unknown. The algorithm requires a set of $N \geq 7$ point correspondences in two views and also the measured relative rotation angle between the views. We show that the problem generically has six solutions (including complex ones). The algorithm has been implemented and tested both on synthetic data and on publicly available real dataset. The experiments demonstrate that the method is correct, numerically stable and robust.Comment: 13 pages, 7 eps-figure

HPV16 E6 gene variations in invasive cervical squamous cell carcinoma and cancer in situ from Russian patients

HPV16 is frequently seen in invasive cervical cancer (ICC) and cervical intraepithelial neoplasia (CIN). Its E6 gene has frequent sequence variations. Although some E6 variants have been reported to have different biochemical or biological properties, they do not show geographical identity. Moreover, the definition of ‘variant’ has been a source of confusion because it has been based on all departures from the ‘prototype’ once isolated randomly from an ICC case. We amplified the HPV16 E6 gene by PCR from fresh-frozen tissue of 104 cases of ICC and CIN from Russian patients and sequenced it in positive cases. We found that 32 of 55 (58.2%) ICC cases and 18 of 49 (36.7%) CIN cases were HPV 16-positive and we could identify 3 groups of E6 variants: group A was characterized by G at nt 350 where group B had T, and group M was a heterogeneous mixture of unique E6 variants; no significant difference existed in the distribution of the different groups between ICC and CIN; the clinically malignant (as defined by FIGO stage) order between the groups was M > A > B in ICC; in the cases with a single HPV16 E6 sequence, coexisting ICC, CIN and normal epithelium in the same patient shared the E6 variant; and 4 cases of ICC had double/multiple E6 variants. The results did not show any importance of E6 variants for ICC progression in Russian women. The results also indicated that the original HPV16 variant persisted during ICC progression, and that at a low frequency, double infections and/or mutation of variants might occur. © 2001 Cancer Research Campaign http://www.bjcancer.co

Adaptive-search template matching technique based on vehicle acceleration for monocular visual odometry system

One of the important tasks of an autonomous mobile vehicle is the reliable and fast estimation of its position over time. This paper presents the development of an adaptive technique to hasten and improve the quality of correlation-based template matching for monocular visual odometry systems that estimate the relative motion of ground vehicles in low-textured environments. Moreover, the factors that can affect the maximum permissible vehicle driving speed were determined and the related equations were derived. The developed system uses a single downward-facing monocular camera installed at an optimum location to avoid the negative effect of directional sunlight and shadows which can disturb the correlation. In addition, the normalized cross-correlation method is implemented to calculate the pixel displacement between image frames. Although this method is highly effective for template matching because of its invariance to linear brightness and contrast variations, it incurs high computational cost. Thus, the optimal sizes of image template and matching search area are selected and their locations are dynamically changed according to vehicle acceleration, in order to achieve a compromise between the performance and the computational cost of correlation. The proposed technique increases the allowable vehicle driving speed and reduces the probability of template false-matching. Moreover, compared to traditional full search matching techniques, the adaptive technique demonstrates high efficiency and accuracy and improves the quality and speed of the correlation with more than 87% of reduction in computational cost

Автоматизированное рабочее место партнера ОАО «Гомсельмаш»

This paper describes a system for structure-and-motion estimation for real-time navigation and obstacle avoidance. We demonstrate it technique to increase the efficiency of the 5-point solution to the relative pose problem. This is achieved by a novel sampling scheme, where We add a distance constraint on the sampled points inside the RANSAC loop. before calculating the 5-point solution. Our setup uses the KLT tracker to establish point correspondences across tone in live video We also demonstrate how an early outlier rejection in the tracker improves performance in scenes with plenty of occlusions. This outlier rejection scheme is well Slated to implementation on graphics hardware. We evaluate the proposed algorithms using real camera sequences with fine-tuned bundle adjusted data as ground truth. To strenghten oar results we also evaluate using sequences generated by a state-of-the-art rendering software. On average we are able to reduce the number of RANSAC iterations by half and thereby double the speed.DIPLEC