We are dealing with the problem of fine-grained vehicle make&model recognition and verification. Our contribution is showing that extracting additional data from the video stream - besides the vehicle image itself - and feeding it into the deep convolutional neural network boosts the recognition performance considerably. This additional information includes: 3D vehicle bounding box used for "unpacking" the vehicle image, its rasterized low-resolution shape, and information about the 3D vehicle orientation. Experiments show that adding such information decreases classification error by 26% (the accuracy is improved from 0.772 to 0.832) and boosts verification average precision by 208% (0.378 to 0.785) compared to baseline pure CNN without any input modifications. Also, the pure baseline CNN outperforms the recent state of the art solution by 0.081. We provide an annotated set "BoxCars" of surveillance vehicle images augmented by various automatically extracted auxiliary information. Our approach and the dataset can considerably improve the performance of traffic surveillance systems.

One limitation of existing fiduciary markers is that the camera motion is tightly limited: the marker (one of the markers) must be visible and it must be observed at a proper scale. This paper introduces a fractal structure of markers similar to matrix codes (such as QR-code or Data Matrix): the Fractal Marker Field. The FMF allows for embedding markers of a virtually unlimited number of scales. At the same time, for each of the scales it guarantees a constant density of markers at that scale over the whole marker field's surface. The Fractal Marker Field can provide unprecedented freedom of motion to camera-based augmented reality applications.

In various applications, a wider area needs to be covered by fiduciary markers but a large marker cannot be used because only a fraction of the area is to be viewed by the camera. Such an area can be covered by a number of small markers with unique identifiers. However, with the camera freely moving in the scene and with occluders present, it is difficult to ensure that at least one of the individual markers is completely visible, unless the markers are small and numerous. In that case, the markers are not recognizable from larger distances.

Detection of lines in raster images is often performed using Hough transform. This paper presents a new parameterization of lines and a modification of the Hough transform–PClines. PClines are based on parallel coordinates, a coordinate system used mostly or solely for high-dimensional data visualization. The PClines algorithm is described in the paper; its accuracy is evaluated numerically and compared to the commonly used line detectors based on the Hough transform. The results show that PClines outperform the existing approaches in terms of accuracy. Besides, PClines are computationally extremely efficient, require no floating-point operations, and can be easily accelerated by different hardware architectures.

This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; however, the algorithm and its implementation are not limited to spectral imaging. The speedups measured on real spectral images are around 60-100{\times} compared to a traditional C implementation compiled with an optimizing compiler. Since common problems in the field of spectral imaging may take hours on a state-of-the-art CPU, the speedup achieved using a graphics card is attractive. The implementation is publicly available in the form of a dynamically linked library, including an interface to MATLAB, and thus may be of help to researchers and engineers using NTF on large problems.

Ray-triangle intersection is an important algorithm, not only in the field of realistic rendering (based on ray tracing) but also in physics simulation, collision detection, modeling, etc. Obviously, the speed of this well-defined algorithm's implementations is important because calls to such a routine are numerous in rendering and simulation applications. Contemporary fast intersection algorithms, which use SIMD instructions, focus on the intersection of ray packets against triangles. For intersection between single rays and triangles, operations such as horizontal addition or dot product are required. The SSE4 instruction set adds the dot product instruction which can be used for this purpose. This paper presents a new modification of the fast ray-triangle intersection algorithms commonly used, which-when implemented on SSE4-outperforms the current state-of-the-art algorithms. It also allows both a single ray and ray packet intersection calculation with the same precomputed data. The speed gain measurements are described and discussed in the paper.