Orientation (computer vision)

About: Orientation (computer vision) is a research topic. Over the lifetime, 17196 publications have been published within this topic receiving 358181 citations.

Delineation and geometric modeling of road networks

Abstract: In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

Three-dimensional orientation tuning in macaque area V4.

Abstract: Tuning for the orientation of elongated, linear image elements (edges, bars, gratings), first discovered by Hubel and Wiesel, is considered a key feature of visual processing in the brain. It has been studied extensively in two dimensions (2D) using frontoparallel stimuli, but in real life most lines, edges and contours are slanted with respect to the viewer. Here we report that neurons in macaque area V4, an intermediate stage in the ventral (object-related) pathway of visual cortex, were tuned for 3D orientation—that is, for specific slants as well as for 2D orientation. The tuning for 3D orientation was consistent across depth position (binocular disparity) and position within the 2D classical receptive field. The existence of 3D orientation signals in the ventral pathway suggests that the brain may use such information to interpret 3D shape.

Specular surface inspection using structured highlight and Gaussian images

Abstract: The structured highlight inspection method uses an array of point sources to illuminate a specular object surface. The point sources are scanned, and highlights on the object surface resulting from each source are used to derive local surface orientation information. The extended Gaussian image (EGI) is obtained by placing at each point on a Gaussian sphere a mass proportional to the area of elements on the object surface that have a specific orientation. The EGI summarizes shape properties of the object surface and can be efficiently calculated from structured highlight data without surface reconstruction. Features of the estimated EGI including areas, moments, principal axes, homogeneity measures, and polygonality can be used as the basis for classification and inspection. The structured highlight inspection system (SHINY) has been implemented using a hemisphere of 127 point sources. The SHINY system uses a binary coding scheme to make the scanning of point sources efficient. Experiments have used the SHINY system and EGI features for the inspection and classification of surface-mounted-solder joints. >

Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids

Abstract: In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image restoration. Its strength relies on providing a simple and, yet, very effective local statistical description of oriented pyramid coefficient neighborhoods via a GSM vector. This can be viewed as a fine adaptation of the model to the signal variance at each scale, orientation, and spatial location. Here, we present an enhancement of the model by introducing a coarser adaptation level, where a larger neighborhood is used to estimate the local signal covariance within every subband. We formulate our model as a BLS estimator using space-variant GSM. The model can be also applied to image deconvolution, by first performing a global blur compensation, and then doing local adaptive denoising. We demonstrate through simulations that the proposed method, besides being model-based and noniterative, it is also robust and efficient. Its performance, measured visually and in L2-norm terms, is significantly higher than the original BLS-GSM method, both for denoising and deconvolution.

Orientation-boosted Voxel Nets for 3D Object Recognition

Abstract: Recent work has shown good recognition results in 3D object recognition using 3D convolutional networks. In this paper, we show that the object orientation plays an important role in 3D recognition. More specifically, we argue that objects induce different features in the network under rotation. Thus, we approach the category-level classification task as a multi-task problem, in which the network is trained to predict the pose of the object in addition to the class label as a parallel task. We show that this yields significant improvements in the classification results. We test our suggested architecture on several datasets representing various 3D data sources: LiDAR data, CAD models, and RGB-D images. We report state-of-the-art results on classification as well as significant improvements in precision and speed over the baseline on 3D detection.