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Episode 4 Rule Based vs Deep Learning Machine Vision

May 7th. 15 minutes read

Transcript:

Welcome to the Deep Dive. Today we’re looking into machine vision. Basically, how computers get to grips with images, how they interpret the visual world. Yeah, it’s a field that’s just exploded, really. It’s everywhere, from medical scans to factory floors. Absolutely. And when you peel back the layers, there seem to be two dominant ways computers are taught to see. That’s right. You’ve got the traditional rule-based machine vision, and then the, well, the newer powerhouse.

deep learning machine vision. And that’s our focus for this deep dive. We want you to walk away with a really clear picture of the core differences between them. Strengths, weaknesses, you know, the key things to understand about each one. Think of it as your quick guide to getting informed on this. We know technical backgrounds vary, so we’ll aim to make it all clear and hopefully pretty engaging. Sounds good. So where should we start? Rule-based. Yeah, let’s unpack rule-based first. What’s the core idea there? OK, so rule-based vision works on

Explicit instructions. Think predefined rules, algorithms that human experts, machine vision engineers create. So they literally write down the rules for seeing. Essentially, yes. They tell the computer exactly what features matter in an image and precisely how to interpret them. Like for identifying a circle, you’d program, look for a continuous curved line where all points are equidistant from the center, something like that. Exactly that kind of logic. The whole process usually follows a few steps. First,

Obviously you capture an image. Right. Then often comes pre-processing. Cleaning the image up, reducing noise, maybe enhancing contrast, filtering, making it easier to analyze. OK. Get the picture ready. Then what? Then comes feature extraction. This is where specific algorithms look for predefined things. Edges, corners, particular shapes, maybe colors or textures. So you’re pulling out the visual ingredients based on what the engineer told the system was important. Precisely. And then the rules kick in.

These are typically if-then statements. Based on those extracted features, a rule might say, if the number of edges detected is greater than 10, then classify this part as defective. Got it. So it’s a direct check against the programmed conditions. Exactly. The system makes its final decision based on whether those rules are met or not. That’s how it identifies patterns or characteristics it was told to look for. It sounds very structured.

Very logical. What are the kind of defining traits of this approach? Well, number one, it’s deterministic. Same input image, you always get the exact same output. No variation. Predictable. Very. It absolutely relies on that explicit programming by people who really understand the imaging and the problem they’re solving. And there’s a lot of effort in what’s called feature engineering. choosing which features the rules should even look at.

Yes, designing and selecting the right features is critical. It’s a very step-by-step analytical process. OK. So where does this method really shine? What are its big advantages? Explainability is a huge one. Because it’s all defined rules, you can easily trace back why the system made a certain decision. It’s transparent. No black boxes. Right. And for tasks that are really well-defined, where the conditions don’t change much, it can be incredibly precise. That’s often fast, too, especially for simpler checks.

which is great for high speed production lines. And data needs? Often lower than deep learning. You don’t necessarily need vast data sets to define rules for simple things. Yeah. And for those straightforward applications, it can be quite cost effective to set up. OK, transparency, precision in the right settings, speed, maybe lower data needs. But I sense a but coming. What are the downsides? Yeah, there are definitely limitations.

The biggest one is probably robustness or lack thereof. If the lighting shifts or the camera angle changes slightly or the object itself looks a bit different. The rules might break. Exactly. The hard-coded rules might just fail because the features don’t look exactly as expected. They aren’t very adaptable. So changing conditions are a problem. A big one. Yeah. If you need the system to handle a new type of object or if the environment changes, you often have to go back and significantly rewrite the rules. It’s not flexible. I can see that.

And designing effective rules for really complex things. It can get incredibly difficult, almost impossible sometimes. It just doesn’t scale well if you have lots of variables or many different types of objects. And maintaining them must be tough too, as things evolve. It can be, yes. Keeping the rules updated and effective can be a challenge. So where do we actually see rule-based vision being used successfully then? What are the typical jobs for it? lots of places still. Think barcode scanning, very defined pattern.

Optical character recognition, OCR for reading text. Right, letters and numbers are pretty standard. Exactly. Precise measurements like checking dimensions on a part, counting objects in a controlled scene, basic quality control, looking for simple known defects in consistent lighting. Think high speed repetitive tasks on an assembly line. OK, got a good feel for rule based now. Structured, precise for the right job, but brittle. That’s a good summary. All right, let’s switch gears.

deep learning machine vision. How does this one work? What’s the fundamental difference? The core shift is huge. Deep learning doesn’t rely on humans writing explicit rules. Instead, it uses artificial neural networks specifically, a type called convolutional neural networks or CNNs, which are great for images to learn directly from data. Learn, so you don’t tell it, look for four corners. Nope, you show the examples, lots of examples typically.

The process starts with collecting a data set of images that are already labeled with the correct answer. pictures of cats labeled cat, pictures of dogs labeled dog. Exactly. Or good part versus bad part. You need that ground truth. Then you design a CNN architecture that’s a whole skill in itself. And the crucial part is training. Training the network. Right. You feed the labeled images into the network. During training, the network adjusts its internal parameters automatically to figure out what patterns and features in the images

are actually predictive of the labels. It learns the connections itself. Wow. So it discovers the important features on a tone. Yes. That’s the magic, really. Once it’s trained, you can give it a new unseen image, and it’ll make a prediction based on what it learned. How does it learn features without being told? That seems like the key. It learns hierarchically. The first layers of the network might learn to detect very simple things, like edges or basic textures, maybe specific colors. The building blocks? Kind of. Then.

Deeper layers learn to combine those simple features into more complex ones, maybe parts of an object like a wheel or an eye. And even deeper layers combine those parts to recognize whole objects. so it builds complexity layer by layer automatically. Exactly. It figures out the relevant features at multiple levels of abstraction all by itself just from the data. Which means no manual feature engineering. Correct. That’s a major, major difference and often a huge time saver compared to

rule-based, especially for complex problems. automatic feature learning. What else characterizes deep learning for vision? Well, it’s fundamentally data-driven. The performance hinges heavily on the data you train it with, quantity, quality, variety. Yeah. It all matters. Garbage in, garbage out, presumably. Very much so. We also talk about end-to-end learning. The network learns the whole pipeline from the raw pixels of the input image straight to the final output or decision. Less distinct steps than rule-based.

Generally, yes. And crucially, they are adaptive. If you get new data or conditions change, you can often retrain the model, update its knowledge, without starting from scratch. Okay, adaptivity sounds like a big plus. Addressing a key weakness of rule-based, what are the other major strengths? Robustness is a big one. They tend to handle variations much better changes in lighting different viewpoints, even if an object is partially blocked or the image is a bit noisy. More like how humans see.

Maybe we can recognize things in imperfect conditions. That’s a good analogy. They often achieve very high accuracy, sometimes even better than humans on specific complex recognition tasks. They can handle incredibly complex and varied image data, and they tend to scale much better to problems with lots of object types or variations. And saving the effort on feature engineering. Absolutely. That allows developers to focus more on the data and the network architecture. It sounds incredibly powerful, but there have to be trade-offs, right?

What are the catches with deep learning? definitely. Firstly, the data requirements. While not always astronomical, high performance, especially for complex tasks, often relies on having a good amount of labeled training data. Which can be expensive or time consuming to get. It can be. Secondly, computational cost. Training these large networks takes a lot of processing power. You typically need powerful GPUs

And training can take hours, days, even weeks. OK, resource intensive training. Then there’s the black box problem, explainability. While a model might be very accurate, figuring out exactly why it made a specific prediction can be really difficult. It’s not as transparent as rule based. So you trust the answer, but you don’t always know the reasoning. Pretty much. Though there’s a lot of research into explainable AI, XAI, trying to shed light on that. Another risk is overfitting. Overfitting. That’s when the model learns the training gated too well.

including its noise and quirks. But then it doesn’t generalize well to new unseen data. It performs poorly in the real world. There are techniques to fight this, but it’s something to watch out for. Right. And you mentioned meeting expertise. Yes. Designing effective neural networks, choosing the right training parameters, tuning them. It requires specialized knowledge and experience. It’s not necessarily simpler than rule-based, just different skills. OK. So data needs.

compute power, explainability issues, overfitting risks, and specialized expertise. Got it. Where does deep learning really make its mark then? What are its killer apps? You see it everywhere. Complex image understanding is needed. Object detection, finding and identifying multiple objects in a scene like an autonomous driving system. OK. Cars seeing pedestrians, other cars, signs. Exactly. Image classification saying this image contains a cat. Facial recognition.

medical image analysis, finding subtle tumors in scans, for instance. Really high stakes applications. Absolutely. Complex quality control, finding defects that are harder to find with simple rules, and things like semantic segmentation, where you label every single pixel in an image with the object class it belongs to, like distinguishing road from sky from building from car at a pixel level. Wow. Okay. Very sophisticated stuff. Definitely pushing the boundaries. All right.

We’ve explored both worlds now, the explicit instructions of rule-based and the data-driven learning of deep learning. Can we sort of put them side by side, a quick comparison of the key differences? let’s break it down. Approach. Rule-based, explicit rules. Deep learning, learning from data. Feature extraction. Rule-based, manual. Deep learning, automatic. Data needs. Rule-based, generally lower. Deep learning, often higher. Benefits from more data. Robustness to variation. Rule-based, lower.

Deep learning, higher. Adaptability. Rule-based, lower. Deep learning, higher through retraining. Accuracy. Rule-based, can be high for simple controlled tasks. Deep learning, often very high, especially for complex variable tasks. Explainability. Rule-based, high. Deep learning, generally low, but improving. Complexity, where’s the hard part? Rule-based, designing effective rules for complex tasks. Deep learning, network design, training.

Tuning needs specialized knowledge. Computation. Rule-based. Lower to moderate, generally. Deep learning. High, especially for training. Scalability. Handling more complexity. Rule-based. Lower. Deep learning. Higher. And typical use cases. Rule-based. Simple, repetitive, controlled environments. Deep learning. Complex, variable, real-world scenarios. Okay, that really crystallizes it. Rule-based gives you transparency and speed for well-defined problems, but it’s rigid.

Correct. Deep learning offers power, adaptability, and high accuracy for the messy real world, but demands more resources and can be harder to interpret. That’s a great way to put it. The choice really boils down to the specific application, its constraints, and its goals. And you mentioned hybrid approaches earlier, people trying to get the best of both. Yes. That’s definitely a growing area, using rule base for some initial checks or very clear-cut cases, and then passing the tougher stuff to a deep learning model. Or using deep learning to maybe even

help generate or refine rules. It’s an active field. Fascinating. So we’ve covered a lot of ground here, really digging into these two fundamental approaches to machine vision. Hopefully, you now have a much clearer sense of how rule-based systems operate versus how deep learning models learn to see. It definitely makes you think differently about things like automated checkouts or your phone unlocking with your face, or even how scientists analyze satellite images. You start wondering, which technique are they using here? Right.

And maybe a final thought to leave you with. As these technologies keep evolving so rapidly, how much more will the lines blur between rule-based logic and deep learning intuition? Could we see entirely new forms of machine vision emerge from their combination, leading to applications we haven’t even conceived of yet?