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Vehicle detection in aerial images is a crucial image processing step for many applications like screening of large areas. In recent years, several deep learning based frameworks have been proposed for object detection. However, these detectors were developed for datasets that considerably differ from aerial images. In this paper, we systematically investigate the potential of Fast R-CNN and Faster...
Instance based human segmentation works on various parameters like labeling of an image at pixel level, partitioning it into distinct instances and the background of the image. The localization, identification and extraction of human image with reliable appearance in a surveillance video are a widely used applications now days. Due to the strong changes in foreground and background and irregularly...
This paper proposes a modified selective search method that generates object proposals on RGB-D data in indoor scenes. The proposed method first applies color flattening to generate monotonous color variations in RGB image data. Then, from the color-flattened image and depth map data, cost function-based segment grouping and depth segmentation are applied to produce desirable segmentation results...
In this paper, we present an efficient edge chain detection algorithm by applying the Helmholtz principle on the gradient magnitude map of an image. An edge chain validation method is proposed which uses the “relative number of false alarms” (RNFA) instead of the traditional “number of false alarms” (NFA). The edge chains are detected first and then validated according to their RNFA values. In this...
A real-time object detection and classification system using FPGA developed for high-speed asymmetric time-stretched optical microscopy (ATOM) framework is presented. Due to the massive amount of data generated by optical frontend, storing the raw data for offline post-processing is slow and impractical for the targeted single cell analysis applications. The proposed FPGA solution eliminates the need...
We propose a multiscale extension of a well-known line segment detector, LSD. We show that its multiscale nature makes it much less prone to over-segmentation, more robust to low contrast and less sensitive to noise, while keeping the parameterless advantage of LSD and still being fast. Moreover, we show that in scenes with little or no feature points, but where it is however possible to perform structure...
Traffic panels contain rich text and symbolic information for transportation and scene understanding. Fast detection of traffic panels facilitates text information extraction but has been paid little attention by the community. In this paper, we propose a fast and robust approach for rectangular traffic panel detection from traffic scene images. Considering the rectangular shape of traffic panels,...
In this paper, we present a method of estimating the vanishing point from the railway environment images. Vanishing point plays a very important role in the machine vision based railway-environment surveillance methods, e.g. estimating the pose of the camera, video segmentation and panorama. In the application of railway-environment surveillance, we most care about the vanishing point corresponding...
Ultrasound medical images are very important component of the diagnostics process. They are widely used since ultrasound is a non-invasive and non-ionizing diagnostics method. As a part of image analysis, edge detection is often used for further segmentation or more precise measurements of elements in the picture. Edges represent high frequency components of an image. Unfortunately, ultrasound images...
The paper presents one approach to blood vessel detection in digital retinal images using ant colony optimization (ACO). Vessel extraction in retina images is a primary step in automatic detection and analysis of the vasculature for diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension, arteriosclerosis and choroidal neovascularization...
AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag...
A new algorithm based in Crεmε Filter, is presented for breast cancer detection. The images obtained show micro calcifications with better contrast, allowing a better detection of cancer. The filter has only one parameter that permitting to observe texture when parameter is changed.
Crest line extraction remains a hard task in image processing. Indeed, these roof edges represent narrow edges on the image surface and whatever undesirable pixel close to or on the crest line may disturb the detection. This communication presents a new crest line detection overall evaluation. Comparing the ground truth contour image and the candidate crest line image, the proposed algorithm is based...
This paper describes methods to evaluate (and train) pixel classifiers when connected components is used as a post-processing step. In previous work the method was used to train a convolutional neural network for image segmentation and we provided pseudo-code for a disjoint-set based algorithm that efficiently calculates the Rand Error and its gradient. This paper describes the modifications we have...
In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better...
With the development and application of the computer, the traditional paper documents can be stored and managed effectively after turning into digital images. In electronization of archives, a single camera is insufficient to gain a whole image clearly. At present, we generally shoot several images for stitching to obtain a complete and clear image. In document stitching, ghost has a quite large influence...
In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity...
In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments in the frame and cluster the results to obtain the region which is most likely to...
Directly connected to the texture appearance, texture granularity is an effective measurement for geographic resources classification, product quality monitoring and image compression ratio selection. However, the application of existing works on texture granularity is limited by intense computation and the dependence on empirically selected parameters that vary among different textures. This paper...
In this paper we explore the application of anomaly detection techniques to tumor voxels segmentation. The developed algorithms work on 3-points dynamic FDG-PET acquisitions and leverage on the peculiar anaerobic metabolism that cancer cells experience over time. A few different global or local anomaly detectors are discussed, together with an investigation over two different algorithms aiming to...
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