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In vision systems, accuracy is the degree to which the computed results match the real-world measurements. Achieved through high-quality datasets, fine-tuning models, and using robust algorithms.

Resolution is the detail an image holds. Higher resolution aids in better feature recognition but demands greater computational power. Trade-offs are often made between resolution and real-time processing needs.

Throughput refers to the number of units of information a system can process over a given time. For real-time vision, maximizing throughput is key for processing data from sensors quickly. Strategies include using efficient data structures, algorithm optimization, and load balancing.

Bandwidth is the data rate supported by a network connection. In computer vision, sufficient bandwidth is essential to transmit high-resolution video streams. Optimization can be through video compression algorithms and ensuring network capacity.

Latency measures the delay between input and response. Critical for time-sensitive applications like autonomous driving. Optimization can include algorithm simplification, efficient hardware, and parallel processing.

Processing Power is the amount of data a computer can handle. For real-time vision systems, having sufficient processing power is crucial. Optimization involves using specialized processors like GPUs, FPGAs, and ASICs.

Scalability is the system's capacity to handle growth in workload. Scalable real-time vision systems can adapt to more significant data volumes and complex tasks, often by using cloud services, modular designs, and elastic resources.

Frame Rate is the frequency at which consecutive images (frames) are displayed. Higher rates improve fluidity of motion. Optimize through code optimization, using more powerful GPUs, and reducing resolution where acceptable.

Memory Utilization reflects how efficiently a vision application uses the system’s memory. Efficient use ensures speed and prevents bottlenecks. Optimization includes memory pooling, efficient caching strategies, and garbage collection.

Robustness refers to a system's ability to handle varied and unexpected inputs without failing. Ensuring robustness involves comprehensive testing, diversity in training datasets, and failure mode analysis.

Jitter refers to the variation in packet arrival time. It's harmful because it can cause image stuttering and affect user experience. Strategies to reduce jitter include using real-time transport protocols, jitter buffers, and ensuring a stable network connection.