This paper reports an architectural concept for achieving dynamic vision with Single-Photon Avalanche Diode (SPAD) detectors. This new concept, demonstrated through a dedicated proof-of-concept sensor chip and an associated camera module, addresses the challenge of large data volume due to frequent laser exposures by optimizing data processing via temporal-contrast-based event filtering. The manuscript analyzes trade-offs between contrast sensitivity, latency, and noise in the proposed discrete DVS architecture and validates the proposal through experimental results obtained with the camera module. The proposed sensor has different operation modes that show potential for augmented reality applications. Benchmarking comparison with competing SPAD-based vision sensors shows latency and power consumption advantages.

Spatial contrast (SC) detection is a fundamental task in computer vision, crucial for simplifying images at an early stage. However, this process typically demands significant power and processing resources. In this paper, we present a novel 128 × 128 pixel asynchronous vision sensor designed to compute SC, thereby eliminating the need to transmit and process intensity frames externally. The sensor is fabricated using 180 nm CMOS technology, with a pixel pitch of$28 \times 26 \mu \text {m}^{2}$. Each pixel provides temporal information relative to its four adjacent pixels to compute the temporal contrast, transmitting data asynchronously. An integrated on-chip Address-Event-Representation (AER) sequencer efficiently manages pixel events, reducing latency requirements on the processing unit. Additionally, the sensor is capable of performing standard imaging operations. Various methods for SC computation are discussed, along with their implementation within the sensor.

The Address Event Representation (AER) protocol is widely used in pixelated neuromorphic systems to implement word-serial asynchronous transmission links. The data is encoded in time or frequency and transferred from the pixel to the receiver in the form of events through a shared channel. Asynchronous sensors benefit from lower data throughput, power consumption, and latency; however, they present several timing limitations to scale the size of the array, since delay in the transmission line and collisions in the readout channel corrupt the timing information. This paper analyzes the effect of increasing the number of pixels in terms of transmission delay and proposes several design guidelines to overcome scaling limitations in high-resolution pixel arrays. The results of the post-layout simulation verify the effectiveness of the design proposals, achieving a reduction of more than 70% in the time delay of the request line.

Thermography imaging has great potential for the diagnosis and follow-up of clinical entities that alter body temperature locally [1]. Infrared (IR) cameras have dropped in price during the last ten years. Nowadays, they are compatible with clinical use because they provide immediate results, do not require physical contact with the patient, and their images are easy to interpret. In particular, vascular anomalies constitute a vast family of clinical entities that alter body temperature and can exploit this emerging imaging modality [2]. Many IR systems devoted to the medical field are available in the market. However, to the best of our knowledge, there is a lack of customizable systems easily adaptable to particular medical application scenarios by incorporating different peripherals or sensors and embedding custom image processing algorithms. Commercial IR systems are limited to image acquisition.

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided.

The optic flow (OF) detection is a complex operation made by biological systems to perform complex tasks as navigation, movement detection or object segmentation within the visual scene. To accomplish this operation with artificial bio-inspired systems is a great challenge. This demo displays a motorized platform [1] with a bio-inspired optical-flow detection sensor which tracks a selected target. The entire system is controlled through a low-cost microcontroller board that has been programmed with very low-level programming language to achieve high performance. The proposed platform can be applied to very different applications scenarios like the obstacle avoidance and the autonomous navigation. Its power consumption and the microcontroller computational load are quite reduced. A low resolution imager with only 18 ×18 pixels is employed.