Deep learning (DL) has revolutionized traditional image processing, pushing the boundaries of Artificial Intelligence (AI) to unlock potential opportunities across industry verticals.
DL helps achieve greater accuracy in object detection, image classification, Simultaneous Localization and Mapping (SLAM), and semantic segmentation compared to traditional image processing techniques.
Several once-impossible problems have now been solved to the point where machines can now outperform humans. However, this does not imply that traditional image processing techniques have become obsolete in the years before the rise of DL.
This article compares the benefits and drawbacks of deep learning and traditional image processing to provide better clarity.
Rapid advancements in DL and device capabilities enhancements, including memory capacity, computing power, power consumption, image sensor resolution, and optics, have accelerated the spread of vision-based applications and improved performance and cost-effectiveness.
Because DL neural networks are trained rather than programmed, applications that use this method often require less fine-tuning and expert analysis. Today’s system has access to a massive amount of video data helps this cause. While CV algorithms are more domain-specific, DL algorithms offer more flexibility because CNN models and frameworks can be retrained using a custom dataset for any application.
Traditional Image processing
Deep learning is overkill because traditional image processing can often solve a problem more accurately and with fewer lines of code than deep learning. The features learned from a deep neural network are specific to the training dataset, which, if poorly constructed, will likely perform poorly for images other than the training set. On the other hand, SIFT and even simple color thresholding and pixel counting algorithms are not class-specific; they are very general and perform the same on any image.
As a result, SIFT and other algorithms are frequently preferred for 3D mesh reconstruction/image stitching applications without specific class knowledge. While large datasets can solve these problems, the massive research effort required for this is not feasible for a closed application. To summarize, one should consider practical feasibility when deciding on the best approach for a computer vision problem.
As an example, consider a product classification problem. Assume the problem is to sort cans of food on a conveyor belt into vegetarian or non-vegetarian categories based on their color – green for vegetarian, red for non-vegetarian. While accurate DL models can be generated by collecting enough training data, traditional image processing, with its simple color thresholding technique, is preferred in this scenario. This example also demonstrates how, in the case of a limited training dataset, DL frequently fails to generalize the task at hand, resulting in over-fitting.
Manually tweaking model parameters is a difficult task because a DNN contains millions of parameters, each with complex interrelationships. As a result, DL models have been branded as black boxes. On the other hand, traditional image processing provides complete transparency and allows one to predict how his or her techniques will perform outside of the training environment. It also allows CV engineers to tweak their parameters to improve their algorithm’s accuracy and performance or investigate their errors when the algorithm fails. Traditional image processing is also preferred for edge computing due to its high performance and low resource usage. This makes traditional image processing more appealing for cloud-based applications, where the high-powered resources required for deep learning applications are prohibitively expensive.
The guidelines below summarize each technology’s common attributes from the preceding discussions. These guidelines also serve as a handy tool for data scientists, novice developers, and business people with no thorough understanding of the subject to make better decisions.
Prefer Deep Learning when:
- There is a lot of training data available to help you make accurate decisions.
- Have a lot of computing power (CPU, GPU, TPU, etc.) to allow intensive model training and good app performance.
- Uncertainty about the positive feature-engineering outcome (i.e., choosing the best feature(s) to achieve the desired result), particularly in unstructured media (audio, text, images).
- Only high-performance devices are allowed to be deployed (i.e., unsuitable for embedded micro-controllers).
- There is little or no domain expertise available.
Stick to traditional image processing when:
- There is a scarcity of (annotated/labeled) data.
- Inadequate storage and processing power.
- A less expensive solution is desired.
- Want to be able to deploy on a variety of hardware.
- There is a lot of domain knowledge present.
A hybrid of deep learning and traditional image processing (dubbed the Hybrid) has gained popularity in recent years due to evidence that it produces better models. Hybrid approaches combine traditional image processing with deep learning to provide the best of both worlds. They’re becoming more popular due to their ability to combine traditional image processing algorithms with versatile and accurate deep learning techniques.
In medical image processing, hybrid approaches have had a lot of success. Mammal review can help doctors determine whether a tumor is benign or malignant, but combining DL and CV capabilities allows us to automate this process and reduce the risk of human error. They’re especially useful in high-performance systems that need to be developed quickly. Over the live feed from a security camera, for example, an image processing algorithm can competently perform face detection. As the next stage in face recognition, these detections can be relayed to a DNN.
This allows the DNN to focus on a small portion of the image, saving a significant amount of computing resources and training time that would otherwise be required to process the entire frame. Fusion can also aid in improving accuracy. Document processing is a classic example, where traditional image processing techniques are used for pre-processing tasks such as noise reduction, skew detection/correction, and line and word localization. When this is followed by OCR using deep techniques, the accuracy improves.