How can you showcase image segmentation in your computer vision portfolio?

I had the opportunity to work with satellite imagery and there are multiple challenges in doing so ? Multiple Spectral Bands ? Varying spatial resolutions of images ? Varying temporal frequency of images ? Multiple file formats ? Several Coordinate Reference Systems (3D to 2D) ? Incompatibility with Computer Vision and Deep Learning Libraries (PIL, Torchvision) These issues make it difficult to work with satellite data. My go-to library to solve some of these issues is TorchGeo, a PyTorch domain library very similar to Torchvision. It provides datasets, samplers, transforms, and pre-trained models specific to geospatial data. The major goal of this library is to make it easy to apply machine learning on geospatial data.

Embarking on an image segmentation journey begins with selecting the right canvas: the domain. It's where passion meets purpose. Whether it's the intricacies of biomedical imaging or the vast expanse of satellite imagery, the domain sets the narrative's tone. The problem, then, is the story's essence. Is it about unveiling hidden tumors or mapping nature's treasures? This clarity not only shapes the technical path but also infuses the endeavor with meaning. In the vast realm of image data, a well-defined domain and problem act as guiding stars, ensuring the quest is both focused and impactful.

It’s vital to narrow down to what use case you’re trying to showcase. For example , I find self driving car related use case super fascinating and for the very purpose my GitHub is filled with such computer vision use-cases , which makes it easy for story telling during interviews as well. While it may be true according to interests, but there are 1000s of use-case which can be worked on and some might also invoke some interest to pursue! Some example domains : 1. Healthcare 2. Autonomous driving 3. Climate change related use-case (Through satellite imagery) 4. Security system

Picking a specific domain and problem is great for direction, especially for solving a new problem or developing out a product. However, with advancements in models like YOLO, there's an increasing ability to adapt to a wide range of environments. This adaptability showcases the versatility of modern image segmentation techniques. While specialization can demonstrate depth, showcasing adaptability can highlight breadth and the dynamic application of one's skills. The advancements in computer vision over recent years have been great, and this momentum is set to continue.

An additional great source for public datasets is Kaggle, provides a variety of image segmentation datasets for those interested in computer vision and image analysis tasks. These datasets cover a range of applications and domains, including object detection, medical imaging, satellite imagery analysis, and more. You can find these datasets in Kaggle's "Datasets" section by searching for keywords like "image segmentation," "object detection," or specific topics you're interested in.

Once you have defined the problem that you want to solve, you need to select a dataset and a tool. There are many different image segmentation datasets available online, and you can choose one that is relevant to your domain and problem. There are also many different image segmentation tools available, both commercial and open source. You can choose a tool that is appropriate for your skill level and the resources that you have available.

Selecting the appropriate model for image segmentation holds significant importance. You have the option to either develop your own image segmentation model or utilize existing state-of-the-art models such as UNET, MASKRCNN, or deeplab. It's worth noting that YOLOv8 also offers support for object segmentation, which can enhance speed while preserving accuracy. Apart from that, It is crucial to thoroughly document the results for current research or potential future use.

Once you have selected a dataset and a tool, you can start implementing a baseline model. A baseline model is a simple model that is used to establish a benchmark for your performance. For example, you could implement a simple thresholding algorithm to segment pixels in your images. Once you have implemented a baseline model, you can evaluate its performance on your dataset.

Rather than relying solely on standard datasets and metrics, conduct extensive error analysis to identify your model's weaknesses. Collect challenging, real-world images that expose flaws not apparent on curated datasets. Iteratively refine your architecture and data augmentation strategies to improve performance on these tough cases. Quantify accuracy on heterogeneous data - don't just report overall metrics. Demonstrate multi-modal capabilities by segmenting natural images, medical scans, satellite imagery, etc. Move beyond pixels to instance and panoptic segmentation. Go beyond segmentation and integrate downstream tasks like detection, tracking, and reconstruction.

Once you have evaluated the performance of your baseline model, you can start improving it. You can do this by trying different algorithms, hyperparameters, and architectures. You can also experiment with different ways of preprocessing and postprocessing your images. If you are using a deep learning model, you can also try training it on different datasets or with different training regimes.

Cloud Native tools plays an important part in image segmentation. The Amazon SageMaker semantic segmentation algorithm is a fine-grained, pixel-level approach helps developing computer vision (CV) applications - tags every pixel in an image with a class label from a predefined set of classes that fundamental for a growing number increasing number of computer vision applications( examples of autonomous vehicles, medical imaging diagnostics, and robot sensing). The MediaPipe Interactive Image Segmenter task is an innovative way to divide images into two regions: the object and everything else - a location in an image helps in estimating the specific location object boundaries and associated data defining the area of the object.

Segmentation is widely used by autonomous vehicle's manufacturers to leverage in their AI and Machine Learning systems to identify and classify various elements of our environment and roads - traffic signs, road contours, pedestrians, vehicles, and other objects. An accuracy of these segmentations help making the correct decisions on the road. Segmentation is used to identify damage nerves and blood vessels in our eyes as part of the diabatic retinopathy detection. Another great example use of image segmentation method helps studying ancient structures and monuments.

Once you have improved your model, you need to visualize and interpret your results. You can use a variety of tools and techniques to visualize your segmentation masks. You can also use metrics such as pixel accuracy, intersection over union (IoU), and mean IoU to evaluate the performance of your model.

Once you have visualized and interpreted your results, you can showcase your project and portfolio. You can do this by writing a blog post, creating a presentation, or building a website. When showcasing your project, be sure to highlight the following: The problem that you solved The dataset and tool that you used The baseline model that you implemented The improvements that you made to your model The results that you achieved The insights that you gained