#ComputerVision_Edge_AI High-Accuracy Real-Time Medical Image Processing on Embedded Systems #RealTimeEmbeddedMedicalImageProcessing C++: cv::setNumThreads(cv::getNumberOfCPUs()) Python: import multiprocessing num_cpus = multiprocessing.cpu_count() cv2.setNumThreads(num_cpus)

As an AI and computer vision expert with over a decade of experience collaborating with esteemed global organizations, my expertise encompasses AI research and development, machine learning, deep learning, Internet of Things (IoT), and model optimization for edge and cloud-based solutions. With a portfolio of 21 publications, three patents, and extensive practical experience in real-time computer vision applications, I have spearheaded groundbreaking projects in generative AI, video analytics, and intelligent systems. Proficient in C++, Python, OpenCV, and advanced GPU optimization, I am recognized for bridging the gap between cutting-edge research and commercially viable products.

lessons learned fixing bug and review codes for my 10 years experince

you need lessons learned after each project so what do you learn during projects.

using floating points

using cast do not compare float/double numbers by ==

CV

Background Subtraction (BGS) algorithms GrabCut algorithm Apply a threshold to the depth map to create a mask that suppresses the distant background. Read frames > Rectify the images (cv2.stereoRectify, cv2.initUndistortRectifyMap, and cv2.remap) > Compute the disparity map (cv2.StereoBM or cv2.StereoSGBM) > Create a depth map > Apply a threshold to the depth map In OpenCV, temporal constraints are used in video analysis to impose rules on how objects or pixels behave over time across consecutive frames. This helps track objects more accurately and make sense of motion, rather than just analyzing individual images.

resize

Upscaling → cv2.INTER_LANCZOS4 (sharp, high quality) or cv2.INTER_CUBIC. Downscaling → cv2.INTER_AREA (built-in antialiasing). Exact scale factors → use fx, fy as floats (avoid rounding dsize). Avoid cumulative rounding → keep data in float (float32/float64) during processing. Gamma-aware resizing (more accurate): linearize to linear RGB, resize, then convert back to sRGB. Pre-blur for large downsamples: light Gaussian blur can suppress aliasing before resize.

VLLM

if the model does not have generating you can create random noisy image as input new models can have multiple input images in the beginning first : mask the object inside the image then edit it keep test of the image original

Camera

Core technologies UVC (USB Video Class) DMA (Direct Memory Access) USB data transfer Video streaming Camera interface Performance and efficiency Low-latency capture High-speed transfer Buffer bypass Chunk-based transfer Real-time processing Frame processing Technical components Method names Data transfer unit Streaming optimization High throughput Driver bypass Frame capture Application context Camera application Live video feed High-performance camera

import cv2

Camera Calibration

Load pre-calibrated camera matrices

NOTE: This requires prior calibration with a checkerboard

and cannot be done in a single line.

See OpenCV documentation for stereoCalibrate.

mtx1, dist1, mtx2, dist2, R, T, E, F = … #### Load matrices from a file

Initialize stereo correspondence algorithm

stereo = cv2.StereoBM.create(numDisparities=16, blockSize=15)

Open cameras (adjust indices as needed)

cap1 = cv2.VideoCapture(0) cap2 = cv2.VideoCapture(1)

while True: ret1, frame1 = cap1.read() ret2, frame2 = cap2.read()

if not ret1 or not ret2:
    break


#### Rectify and process the images
undistort1 = cv2.undistort(frame1, mtx1, dist1)
undistort2 = cv2.undistort(frame2, mtx2, dist2)

#### Compute the disparity map
disparity = stereo.compute(cv2.cvtColor(undistort1, cv2.COLOR_BGR2GRAY),
                          cv2.cvtColor(undistort2, cv2.COLOR_BGR2GRAY))

#### Create a mask by thresholding the disparity (adjust the value)
#### A low disparity means the object is far away (background)
_, mask = cv2.threshold(disparity, 50, 255, cv2.THRESH_BINARY_INV)

#### Apply the mask to the original frame
masked_frame = cv2.bitwise_and(frame1, frame1, mask=mask)

#### Display the result
cv2.imshow('Background Suppressed', masked_frame)


if cv2.waitKey(1) & 0xFF == ord('q'):
    break

cap1.release() cap2.release() cv2.destroyAllWindows() —

Relational databases store multi-modal, multi-scale information. The database level has a relational/temporal structure. Relational Graph Neural Networks (RelGNNs) use composite messages.

Relational Foundation Models (RFMs) are a type of large language model that focus on learning, usability, and systems optimization and inference. They use a unique approach called relational transformer zero-shot prompting, which allows them to make predictions without prior training on specific data. RFMs also offer highly efficient supervised fine-tuning, enabling them to quickly adapt to new tasks and datasets.

Proactive decision intelligence (DI) is another key feature of RFMs. DI involves predicting and forecasting outcomes, which can be particularly useful in fields like biology where scientists need to make informed decisions based on complex data. RFMs can perform DI in real-time, making predictions for any use case on any data in seconds. However, it’s important to note that RFMs are not designed for batch prediction over billions of entities.

Artificial intelligence (AI) is being increasingly used in various scientific fields, including biology. AI agents are being developed to assist scientists in their research and experiments, helping them to analyze data more efficiently and make more accurate predictions.

Identify your growth areas. Think big-picture and long-term to ground your Professional Growth Plan (PGP) in your core values and desired career direction. Identify your career drivers, considering what matters most to you in terms of career growth, such as challenges, advancement, influence, or security. Determine your desired brand or “image,” defining what you want to be known for in your career. Set career goals, visualizing your career trajectory over the next few years. If you’re uncertain about your future, explore potential career paths or target roles that interest you or warrant further exploration. Identify areas of work that intrigue you or warrant further learning.

Focus on your strengths and areas for improvement. To grow professionally, build skills that excel in your current role and broaden or deepen your experience for career advancement. Your last performance review or development conversation with your lead can provide valuable insights for this section. Identify tasks in your current job that contribute to your long-term goals and that you want to emphasize or perform more frequently. Determine the skills you need to develop in your current role or aspire to in other areas.

Develop an action plan, being specific about how you’ll build the necessary skills. Effective development plans incorporate activities from each of the three E’s: Experience, Exposure, and Education. Here are some examples of each: Peer coaching Stretch assignments Special projects Transfers/rotations For future reference, your PGP will evolve as your interests, skills, and experience change. Review it quarterly to stay accountable to your action items and share your progress with your lead.

I have a Windows PC with an NVIDIA GPU and need to work on other PCs and a laptop without NVIDIA GPUs. For remote work or weekend coding and research, I want seamless project continuity. One solution is to use WSL (Windows Subsystem for Linux) and create a portable Linux environment. This allows me to use the same Linux setup on any Windows machine, even without an NVIDIA GPU, while keeping my projects, code, and large datasets accessible on an external SSD.

  1. To import WSL on a new PC without Ubuntu or WSL installed, check the WSL version and update:
  3. wsl --version
wsl --update
  4. Import the exported .tar file into the same folder path as the main PC:
  6. wsl --import 22 D:\\\\WSL\\\\DistroName D:\\\\WSL\\\\22\\\\ubuntu22.tar
wsl --shutdown

Notes: