A comparative evaluation of interest point detectors and local descriptors for visual SLAM

Abstract In this paper we compare the behavior of different interest points detectors and descriptors under the conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM). We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors, under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes. We believe that this information will be useful when selecting an appropriat

Combining depth and intensity images to produce enhanced object detection for use in a robotic colony

Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in de-tail, but interact with that ambient space. Many object recognition techniques ex-ist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony

Rectification from Radially-Distorted Scales

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle images that contain nearly any type of coplanar repeated content. We demonstrate a principled approach to generating stable minimal solvers by the Grobner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the solvers give accurate rectifications from noisy measurements when used in a RANSAC-based estimator. The proposed solvers demonstrate superior robustness to noise compared to the state-of-the-art. The solvers work on scenes without straight lines and, in general, relax the strong assumptions on scene content made by the state-of-the-art. Accurate rectifications on imagery that was taken with narrow focal length to near fish-eye lenses demonstrate the wide applicability of the proposed method. The method is fully automated, and the code is publicly available at https://github.com/prittjam/repeats.Comment: pre-prin

Mo Músaem Fíorúil: a web-based search and information service for museum visitors

Abstract. We describe the prototype of an interactive, web-based, museum artifact search and information service. Mo Músaem Fíorúil clusters and indexes images of museum artifacts taken by visitors to the museum where the images are captured using a passive capture device such as Microsoft's SenseCam [1]. The system also matches clustered artifacts to images of the same artifact from the museums o cial photo collection and allows the user to view images of the same artifact taken by other visitors to the museum. This matching process potentially allows the system to provide more detailed information about a particular artifact to the user based on their inferred preferences, thereby greatly enhancing the user's overall museum experience. In this work, we introduce the system and describe, in broad terms, it's overall functionality and use. Using different image sets of artificial museum objects, we also describe experiments and results carried out in relation to the artifact matching component of the system

Automatic annotation of tennis games: an integration of audio, vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level

Enhancing real-time human detection based on histograms of oriented gradients

In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square-blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature selection and Support Vector Machine as weak classifier, we build up a real-time human classifier with an excellent detection rate.Peer Reviewe

Determination of fatty acid composition in seed oil of rapeseed (Brassica napus L.) by mutated alleles of the FAD3 desaturase genes

One of the goals in oilseed rape programs is to develop genotypes producing oil with low linolenic acid content (C18:3, ≤3%). Low linolenic mutant lines of canola rapeseed were obtained via chemical mutagenesis at the Plant Breeding and Acclimatization Institute – NRI, in Poznan, Poland, and allele-specific SNP markers were designed for monitoring of two statistically important single nucleotide polymorphisms detected by SNaPshot analysis in two FAD3 desaturase genes, BnaA.FAD3 and BnaC.FAD3, respectively. Strong negative correlation between the presence of mutant alleles of the genes and linolenic acid content was revealed by analysis of variance. In this paper we present detailed characteristics of the markers by estimation of the additive and dominance effects of the FAD3 genes with respect to particular fatty acid content in seed oil, as well as by calculation of the phenotypic variation of seed oil fatty acid composition accounted by particular allele-specific marker. The obtained percentage of variation in fatty acid composition was considerable only for linolenic acid content and equaled 35.6% for BnaA.FAD3 and 39.3% for BnaC.FAD3, whereas the total percentage of variation in linolenic acid content was 53.2% when accounted for mutations in both genes simultaneously. Our results revealed high specificity of the markers for effective monitoring of the wild-type and mutated alleles of the Brassica napus FAD3 desaturase genes in the low linolenic mutant recombinants in breeding programs

Semantic-Context-Based Augmented Descriptor For Image Feature Matching

Abstract. This paper proposes an augmented version of local features that enhances the discriminative power of the feature without affecting its invariance to image deformations. The idea is about learning local features, aiming to estimate its semantic, which is then exploited in conjunction with the bag of words paradigm to build an augmented feature descriptor. Basically, any local descriptor can be casted in the proposed context, and thus the approach can be easy generalized to fit in with any local approach. The semantic-context signature is a 2D histogram which accumulates the spatial distribution of the visual words around each local feature. The obtained semantic-context component is concatenated with the local feature to generate our proposed feature descriptor. This is expected to handle ambiguities occurring in images with multiple similar motifs and depicting slight complicated non-affine distortions, outliers, and detector errors. The approach is evaluated for two data sets. The first one is intentionally selected with images containing multiple similar regions and depicting slight non-affine distortions. The second is the standard data set of Mikolajczyk. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.

Patch-Based Experiments with Object Classification in Video Surveillance

We present a patch-based algorithm for the purpose of object classification in video surveillance. Within detected regions-of-interest (ROIs) of moving objects in the scene, a feature vector is calculated based on template matching of a large set of image patches. Instead of matching direct image pixels, we use Gabor-filtered versions of the input image at several scales. This approach has been adopted from recent experiments in generic object-recognition tasks. We present results for a new typical video surveillance dataset containing over 9,000 object images. Furthermore, we compare our system performance with another existing smaller surveillance dataset. We have found that with 50 training samples or higher, our detection rate is on the average above 95%. Because of the inherent scalability of the algorithm, an embedded system implementation is well within reach