Configurable 3D-integrated focal-plane sensor-processor array architecture

A mixed-signal Cellular Visual Microprocessor architecture with digital processors is described. An ASIC implementation is also demonstrated. The architecture is composed of a regular sensor readout circuit array, prepared for 3D face-to-face type integration, and one or several cascaded array of mainly identical (SIMD) processing elements. The individual array elements derived from the same general HDL description and could be of different in size, aspect ratio, and computing resources

High dynamic range perception with spatially variant exposure

In this paper we present a method capable of perceiving high dynamic range scene. The special feature of the method is that it changes the integration time of the imager on the pixel level. Using CNN-UM we can calculate the integration time for the pixels, and hence low dynamic range integration type CMOS sensors will be able to perceive high dynamic range scenes. The method yields high contrast without introducing non-existing edges

Intraframe Scene Capturing and Speed Measurement Based on Superimposed Image: New Sensor Concept for Vehicle Speed Measurement

A vision based vehicle speed measurement method is presented in this paper. The proposed intraframe method calculates speed estimates based on a single frame of a single camera. With a special double exposure, a superimposed image can be obtained, where motion blur appears significantly only in the bright regions of the otherwise sharp image. This motion blur contains information of the movement of bright objects during the exposure. Most papers in the field of motion blur are aiming at the removal of this image degradation effect. In this work, we utilize it for a novel speed measurement approach. An applicable sensor structure and exposure-control system are also shown, as well as the applied image processing methods and experimental results. © 2016 Mate Nemeth and Akos Zarandy

Error analysis of attitude estimation with focal-plane processors for guidance for mobile robots

In this paper the results of the error analysis of four different feature point based attitude estimator algorithm is introduced. The algorithms was tested in simulation with realistic flight paths and camera models. With these results a best performing candidate algorithm can be chosen for a given focal-plane processor and for the given scenarios

Classification of Holograms with 3D-CNN

A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image