Video Enhancement and Dynamic Range Control of HDR Sequences for Automotive Applications

CMOS video cameras with high dynamic range (HDR) output are particularly suitable for driving assistance applications, where lighting conditions can strongly vary, going from direct sunlight to dark areas in tunnels. However, common visualization devices can only handle a low dynamic range, and thus a dynamic range reduction is needed. Many algorithms have been proposed in the literature to reduce the dynamic range of still pictures. Anyway, extending the available methods to video is not straightforward, due to the peculiar nature of video data. We propose an algorithm for both reducing the dynamic range of video sequences and enhancing its appearance, thus improving visual quality and reducing temporal artifacts. We also provide an optimized version of our algorithm for a viable hardware implementation on an FPGA. The feasibility of this implementation is demonstrated by means of a case study

Erratum: A microscopic view on the Mott transition in chromium-doped V 2 O 3

Nature Communications 1, Article number: 105 (2010); published: 02 November 2010; updated: 17 January 2012. In Figure 2 of this Article, panel labels c and d were inadvertently switched. A typographical error was also introduced in the last sentence of the legend, which should have read 'The scale bar in panel c represents 10 μm'

A robust tracking algorithm for super-resolution reconstruction of vehicle license plates

In the installation of video surveillance systems it is quite common to look for a compromise for what concerns the focal length of the optics of the cameras: if on one hand the choice of wide angle lens, i.e. lenses with a large angle of view, permits a global inspection of the area to be monitored, so considerably limiting the "dead zones", on the other hand this approach often jeopardizes the readability of important details in the image. This problem is particularly significant in the identification of the license plates of vehicles, since most often they are represented in a very small area of the image, so that only a quite low resolution version of the license plate is available for identification. Alternatively, the use of narrow angle cameras facilitates the recognition but can be taken only in very limited and specific cases, i.e. only if the spatial location of the license plate is known a priori. Consequently, the police personnel often needs to extract, from low resolution and noisy sequences of images, essential information for the recognition of the targets. In order to solve the problem, we can observe that although the single image is not detailed enough to allow a proper identification, on the other hand the availability of an entire sequence, composed by several images of the same target, can lead, through super-resolution techniques[1-3], to the reconstruction of an image with a resolution higher than the original one, in a process which aims at reversing the process that from the actual scene generated the low resolution image. However, an essential point, in order to obtain a good reconstruction, is the ability to identify in an extremely precise way and with sub-pixel resolution the exact position of the license plate area in each single frame. In this paper we have optimized each step of the entire process that from a low resolution, real world sequence, leads to a super-resolution image. The procedure must identify the target, track its trajectory along the sequence with great precision, extract its position in each frame and eventually combine all the low resolution images in a higher resolution version of the target. The procedure we propose follows a semi automatic approach and consists in several steps. Firstly the user identifies, in the first frame of the sequence, several points of interest (POIs) of the vehicle located on the plane which contains the license plate, including the corners of the license plate itself. Than the system automatically estimates, frame by frame, the new positions of all these POIs. This phase of the process makes use of genetic algorithms in order to solve a constrained optimization problem which aims at identifying the most likely location of each POI constrained by the fact that, since all the points belong to the same rigid body, their position in the different frames must be described by an appropriate perspective transformation [4,5]. The proposed system is able to achieve excellent performance in the tracking of the target with sub-pixel resolution. The final step of the process is the reconstruction phase, where each frame is perspective transformed, aligned, cropped, de-convolved and interpolated to higher resolution. Eventually, all these data are combined together in a super-resolution image

A 10 kb/s Video Coding Technique Based on Spatial Transformation

This paper describes a method for coding QCIF image sequences at 10 kb/s and 10 frame/s. The proposed algorithm performs motion compensation between consecutive frames using a spatial transformation. It is divided in two steps: the first one consists of the motion estimation and compensation, the second one includes the prediction error processing and transmission. This technique is of help in overcoming the limits presented at very low bit-rate by traditional hybrid block-based techniques

Image compression using a perceptron

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A new FPGA-based architecture for iterative and space-variant image processing

We propose a new FPGA-based architecture able to speed up the Lucy-Richardson algorithm (LRA) for space-variant image deconvolution. The architecture exploits the possibility to distribute data into different memory blocks in the FPGA. In such a way, the algorithm execution is split into several channels operating in parallel. Since the LRA is implemented via an iterative, space-variant convolution, the strategies adopted in this paper can be exploited in other similar image processing algorithms

A Novel Architecture for Real-Time Space Variant Image Deconvolution

In this work we propose a new FPGA-based architecture able to speed up the Lucy-Richardson algorithm (LRA) for space-variant image deconvolution. The architecture exploits the possibility to distribute data among different memory blocks in the FPGA. In this way, the algorithm execution is split into several channels operating in parallel. Since the LRA is implemented via an iterative, space variant convolution, the strategies adopted in this paper can be exploited in other similar image processing algorithm

A robust tracking algorithm for super-resolution reconstruction of vehicle license plates

We propose a novel, very robust method for tracking a vehicle license plate in a sequence of low-resolution frames acquired by a video surveillance camera in order to reconstruct the license plate view in a super-resolution image. The tracking method is able to follow the license plate corners position with a sub-pixel resolution and to compensate for small spatial movements of the target during the motion by adopting a perspective transformation. In the reconstruction of the target each frame is perspective transformed, aligned, cropped, de-convolved and interpolated to higher resolution. Eventually the data are combined into a super-resolution image

Structure from Linear Motion (SfLM): An On-the-Go Canopy Profiling System Based on Off-the-Shelf RGB Cameras for Effective Sprayers Control

6Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage, and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard’s row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speednoneopenDe Bortoli, Luca; Marsi, Stefano; Marinello, Francesco; Carrato, Sergio; Ramponi, Giovanni; Gallina, PaoloDe Bortoli, Luca; Marsi, Stefano; Marinello, Francesco; Carrato, Sergio; Ramponi, Giovanni; Gallina, Paol