A 1 millisecond (1-ms) vision system, which processes videos at 1000 frames per second (FPS) within 1 ms/frame delay, plays an increasingly important role in fields such as robotics and factory automation. Superpixel as one of the most extensively employed image oversegmentation methods is a crucial pre-processing step for reducing computations in various computer vision applications. Among the different superpixel methods, simple linear iterative clustering (SLIC) has gained widespread adoption due to its simplicity, effectiveness, and computational efficiency. However, the iterative assignment and update steps in SLIC make it challenging to achieve high processing speed. To address this limitation and develop a SLIC superpixel segmentation system with a 1 ms delay, this paper proposes grid sample based temporal iteration. By leveraging the high frame rate of the input video, the proposed method distributes the iterations into the temporal domain, ensuring that the system's delay keeps within one frame. Additionally, grid sample information is added as initialization information to the obtained superpixel centers for enhancing the stability of superpixels. Furthermore, a selective label propagation based pipeline architecture is proposed for parallel computation of all the possibilities of label propagation. This eliminates data dependency between adjacent pixels and enables a fully pipelined system. The evaluation results demonstrate that the proposed superpixel segmentation system achieves boundary recall and under-segmentation error comparable to the original SLIC algorithm. When considering label consistency, the proposed system surpasses the performance of state-of-the-art superpixel segmentation methods. Moreover, in terms of hardware performance, the proposed system processes 1000 FPS images with 0.985 ms/frame delay.

High frame rate and ultra-low delay vision system, which can finish reading and processing of 1000fps sequence within 1ms/frame, draws increasing attention in the field of robotics that requires immediate feedback from image process core. Meanwhile, tracking task plays an important role in many computer vision applications. Among various tracking algorithms, Lucas Kanade (LK)-based template tracking, which tracks targets with high accuracy over the sub-pixel level, is one of the keys for robotic applications, such as factory automation (FA). However, the substantial spatial iterative processing and complex computation in the LK algorithm, make it difficult to achieve a high frame rate and ultra-low delay tracking with limited resources. Aiming at an LK-based template tracking system that reads and processes 1000fps sequences within 1ms/frame with small resource costs, this paper proposes: 1) High temporal resolution-based temporal iterative tracking, which maps the spatial iterations into the temporal domain, efficiently reduces resource cost and delay caused by spatial iterative processing. 2) Label scanner-based multi-stream spatial processing, which maps the local spatial processing into the labeled input pixel stream and aggregates them with a label scanner, makes the local spatial processing in the LK algorithm possible be implemented with a small resource cost. Algorithm evaluation shows that the proposed temporal iterative tracking performs dynamic tracking, which tracks object with coarse accuracy when it's moving fast and achieves higher accuracy when it slows down. Hardware evaluation shows that the proposed label scanner-based multi-stream architecture makes the system implemented on FPGA (zcu102) with resource cost less than 20%, and the designed tracking system supports to read and process 1000fps sequence within 1ms/frame.